diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6ac5b187eb1388d86548ec03c864e96bc2c5cc91
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,166 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Streamlit secrets
+.streamlit/
+
+# C extensions
+*.so
+
+# Virtual Environment 
+venv-app-ai-ds/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/README.md b/README.md
index 9256e8363c2e8139e1a3c9cdf4c3db40e4e7b2d3..4c1f03e8a2bb61623efd80e7ade7791613c6e015 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,14 @@
----
-title: App Ai Ds Hec
-emoji: 🐨
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.31.1
-app_file: app.py
-pinned: false
-license: mit
----
+# AI and Data Science examples 🧠
+Space for the Streamlit "AI and Data Science examples" HEC Paris app.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+The app is structured in 5 pages:
+- Supervised vs Unsupervised
+- Time Series Analysis
+- Sentiment Analysis
+- Object detection
+- Recommendation system
+
+Each page contains one or more real-life use cases of AI.
+Some of these use cases include electrical power consumption forecasting or customer segmentation 
+
+Other pages on image segmentation and topic modeling are currently being developped. 
diff --git a/data/classification/credit_score/credit_score_cm_train b/data/classification/credit_score/credit_score_cm_train
new file mode 100644
index 0000000000000000000000000000000000000000..bed6bbe15553352ff858604d93d2efe18fc19118
Binary files /dev/null and b/data/classification/credit_score/credit_score_cm_train differ
diff --git a/data/classification/credit_score/credit_score_test_pp.pkl b/data/classification/credit_score/credit_score_test_pp.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0d7171987b64e9779691614027e34b7ce164703e
--- /dev/null
+++ b/data/classification/credit_score/credit_score_test_pp.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0fbd76a90f5289377d21c2d39f377cb73e13de1c71f3818c7b0ea71f46a29ac
+size 241322
diff --git a/data/classification/credit_score/credit_score_test_raw.pkl b/data/classification/credit_score/credit_score_test_raw.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..655f182dac596da92149b1d9954a86e53ab9b755
--- /dev/null
+++ b/data/classification/credit_score/credit_score_test_raw.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2659d847aeb751f63a49e15b6bdc501be32eaddfaba9b33ca86f279065559f2
+size 103703
diff --git a/data/classification/credit_score/credit_score_train_raw.pkl b/data/classification/credit_score/credit_score_train_raw.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..832458c243b6a1c49b5bc245694ee36eeb076b57
--- /dev/null
+++ b/data/classification/credit_score/credit_score_train_raw.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d218682f6e67a5c9f81d227fd90362976185aa78958ab432d6561b6dfd960a4
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diff --git a/data/clustering/clean_marketing.pkl b/data/clustering/clean_marketing.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d49b35ad312567c039a52e337e3009649be511fd
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+++ b/data/clustering/clean_marketing.pkl
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+version https://git-lfs.github.com/spec/v1
+oid sha256:7bde1e077f04583237c0029abf25f841d9100d9050375fbec00c328f14c5c1b2
+size 284225
diff --git a/data/clustering/results/results_2_clusters.pkl b/data/clustering/results/results_2_clusters.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d2d6dbe81dff5242645ecf875b2959f6272cf8f3
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+++ b/data/clustering/results/results_2_clusters.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1155
diff --git a/data/clustering/results/results_3_clusters.pkl b/data/clustering/results/results_3_clusters.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..54cc47399b2431dae8f32190509c444194655b7b
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+version https://git-lfs.github.com/spec/v1
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+size 1279
diff --git a/data/clustering/results/results_4_clusters.pkl b/data/clustering/results/results_4_clusters.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5f7403aa82c27139dbe591317b4b282d45114790
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+++ b/data/clustering/results/results_4_clusters.pkl
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/clustering/results/results_5_clusters.pkl b/data/clustering/results/results_5_clusters.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..82ee3b9152c51a116b05cd8465b65c72d3fd32d1
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diff --git a/data/clustering/results/results_6_clusters.pkl b/data/clustering/results/results_6_clusters.pkl
new file mode 100644
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diff --git a/data/hotels/booking_df.csv b/data/hotels/booking_df.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c43b0dc54720d9aca6cf70e4c134c25105dd7ef9
--- /dev/null
+++ b/data/hotels/booking_df.csv
@@ -0,0 +1,2560 @@
+Hotel Name,Location,Rating,Room Score,Room Type,Bed Type,Price,City,Country,twins,fulls,queen,king,sofa beds,bunk beds,futon beds,Number of bed
+Krabi La Playa Resort - SHA Plus,Ao Nang Beach,8.2,8.6,Deluxe Double or Twin Room,   1 double or 2 twins,146.026,Krabi,Thailand,2,0,0,0,0,0,0,2
+Rawai VIP Villas & Kids Park,Rawai Beach,8.3,8.9,2 Bedroom Pool Villa,2 queen beds,435.384,Phuket,Thailand,0,0,2,0,0,0,0,2
+"FuramaXclusive Sathorn, Bangkok","Bang Rak, Bangkok",7.7,8.1,Executive Double Room,1 full bed,146.24,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Bo Phut Resort and Spa - SHA Plus,Bophut,9.3,9.6,Villa with Garden View,   1 double or 2 twins,621.072,Ko Samui,Thailand,2,0,0,0,0,0,0,2
+Abloom Exclusive Serviced Apartments,"Phaya Thai, Bangkok",8.1,8.4,One Bedroom Deluxe(Flash Sales),1 full bed,178.109,Bangkok,Thailand,0,1,0,0,0,0,0,1
+"Bandara Suites Silom, Bangkok - SHA Extra Plus","Bang Rak, Bangkok",8.3,8.7,Studio,   1 double or 2 twins,176.217,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Bhukitta Boutique Hotel,Phuket,7.6,8.0,Superior Double or Twin Room,1 queen bed,84.658,Phuket,Thailand,0,0,1,0,0,0,0,1
+Hotel Verdigris,Phuket,9.4,9.7,Twin Room,2 twin beds,407.481,Phuket,Thailand,2,0,0,0,0,0,0,2
+Spacious apartment with Ocean view in Panwa,Phuket,9.1,9.4,Two-Bedroom Apartment,"3 beds (2 twins, 1 king)",173.845,Phuket,Thailand,2,0,0,1,0,0,0,3
+Explorar Koh Samui - Adults Only Resort and Spa,Mae Nam,9.1,9.5,Deluxe Double or Twin Room with Balcony,1 king bed,384.935,Ko Samui,Thailand,0,0,0,1,0,0,0,1
+Waterfront Suites Phuket by Centara,Karon Beach,8.5,8.6,One-Bedroom Suite with Sea View,"3 beds (2 twins, 1 sofa bed)",202.186,Phuket,Thailand,2,0,0,0,0,0,0,3
+Art Mai Gallery Nimman Hotel Chiang Mai - SHA Plus,"Nimmanhaemin, Chiang Mai",8.3,8.8,Premier Art Room,   1 double or 2 twins,199.88,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+The Leaf Oceanside by Katathani - SHA Extra Plus,"Nang Thong Beach, Khao Lak",8.6,8.9,Cottage Double or Twin Room,   1 double or 2 twins,101.895,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+Twin Lotus Resort and Spa - SHA Plus - Adult Only Hotel,"Klong Dao Beach, Ko Lanta",8.5,9.0,Villa with Garden View,1 king bed,253.604,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Siam Subway Hostel and Café,"Bangkok Old Town, Bangkok",9.4,9.5,Deluxe King Room with Shared Bathroom,1 queen bed,98.66,Bangkok,Thailand,0,0,1,0,0,0,0,1
+TheView Chaengwattana14 14,"Don Muang, Bangkok",8.6,9.0,One-Bedroom Apartment,1 queen bed,66.254,Bangkok,Thailand,0,0,1,0,0,0,0,1
+The Blanket Hotel Phuket Town - SHA Plus,Phuket,8.0,8.6,Deluxe King or Twin Room,2 twin beds,126.738,Phuket,Thailand,2,0,0,0,0,0,0,2
+The Siamese Hotel,North Pattaya,8.7,8.9,Deluxe Double Room,1 king bed,144.177,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Paradise Resort Phi Phi-SHA Plus,"Long Beach, Phi Phi Islands",7.9,0.0,Thai Modern Apartment,1 full bed,168.207,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+S3 Siam Bangkok Hotel,"Pathumwan, Bangkok",8.2,8.7,Superior King Room,1 king bed,194.085,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Haad Salad Villa,Salad Beach,8.6,8.5,Standard Bungalow,1 queen bed,115.21900000000001,Ko Phangan,Thailand,0,0,1,0,0,0,0,1
+Maven Stylish Hotel Hua Hin,Hua Hin,8.6,9.3,Superior Double or Twin Room with City View,2 twin beds,167.829,Hua Hin,Thailand,2,0,0,0,0,0,0,2
+Sawasdee House,"Khaosan, Bangkok",7.7,0.0,Superior Double Room with Window,1 queen bed,90.57300000000001,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Casa De Mar - SHA Plus,"Chaweng Beachfront, Chaweng",8.2,8.4,Superior Double or Twin Room,1 king bed,265.286,Chaweng,Thailand,0,0,0,1,0,0,0,1
+Luxury Apartment at Title Residencies Naiyang Beach,Phuket,7.3,8.3,Apartment with Pool View,1 queen bed,84.427,Phuket,Thailand,0,0,1,0,0,0,0,1
+I Pavilion Hotel Phuket - SHA Extra Plus,Phuket,8.5,8.9,Superior Double or Twin Room,2 twin beds,78.09100000000001,Phuket,Thailand,2,0,0,0,0,0,0,2
+Chura Samui - SHA Plus,"Chaweng Beachfront, Chaweng",8.2,8.7,Grand Deluxe Double or Twin Room,Multiple bed types,163.572,Chaweng,Thailand,0,0,0,0,0,0,0,0
+Sabaii Bay Resort,Ban Tai,7.2,0.0,Standard Double or Twin Room,   1 double or 2 twins,119.417,Ko Phangan,Thailand,2,0,0,0,0,0,0,2
+Assava Dive Resort - SHA Plus,"Chalok Baan Kao, Ko Tao",8.5,8.4,Superior Double Room,1 king bed,120.98,Ko Tao,Thailand,0,0,0,1,0,0,0,1
+Moondance Magic View Bungalow,Ko Tao,8.5,8.5,Double Room with Balcony and Sea View,1 full bed,103.20400000000001,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Buri Sriping Riverside Resort & Spa - SHA Extra Plus,"Wat Ket, Chiang Mai",9.1,9.4,Deluxe Room,1 king bed,271.719,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Armoni Patong Beach Hotel,Patong Beach,7.4,0.0,Superior Double or Twin Room,   1 double or 2 twins,60.652,Phuket,Thailand,2,0,0,0,0,0,0,2
+Tharaburi Resort,"Mueang Kao, Sukhothai",8.4,8.7,Deluxe Plus,1 king bed,192.976,Sukhothai,Thailand,0,0,0,1,0,0,0,1
+The Top Patong Resort,Patong Beach,6.3,0.0,Superior Double or Twin Room with City View,1 full bed,55.453,Phuket,Thailand,0,1,0,0,0,0,0,1
+Patong Lodge Hotel - SHA Extra Plus,"Kalim Beach, Patong Beach",7.3,0.0,Triple Room with Balcony,"2 beds (1 twin, 1 queen)",109.627,Phuket,Thailand,1,0,1,0,0,0,0,2
+Phi Phi Bayview Resort,"Tonsai Bay, Phi Phi Islands",7.2,0.0,Premier Deluxe Villa,1 full bed,120.462,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+V20 Boutique Jacuzzi Hotel - SHA Extra Plus,"Chatuchak, Bangkok",9.0,9.3,Family Room with Balcony,1 king bed,135.083,Bangkok,Thailand,0,0,0,1,0,0,0,1
+"Anantasila Villa by the sea, Hua Hin - SHA Extra Plus","Khao Takiab, Hua Hin",8.9,9.3,Double Room with Balcony and Pool View,2 queen beds,367.133,Hua Hin,Thailand,0,0,2,0,0,0,0,2
+U Sabai Living Hotel - SHA Certified,Patong Beach,8.7,9.0,Standard Double Room,1 king bed,51.571,Phuket,Thailand,0,0,0,1,0,0,0,1
+Khao thalu guest house,Ban Muang Wan,0.0,0.0,Double Room with Garden View,1 king bed,64.69500000000001,Not specified,Thailand,0,0,0,1,0,0,0,1
+Majestic Suites Hotel,"Khlong Toei, Bangkok",8.0,8.4,Deluxe Room,1 full bed,130.38400000000001,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Muan Hotel,"Si Phum, Chiang Mai",8.6,8.9,Family Room with Private Bathroom,"2 beds (1 full, 1 bunk bed)",109.797,Chiang Mai,Thailand,0,1,0,0,0,0,0,2
+Sabai Sabana - formerly Sabai Wing,Pattaya,7.6,8.1,Standard Twin Room,2 twin beds,78.459,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Marina House MUAYTHAI Ta-iad Phuket,Chalong,8.4,8.5,Standard Double Room,1 queen bed,69.733,Phuket,Thailand,0,0,1,0,0,0,0,1
+Koh Tao Cabana,"Sairee Beach, Ko Tao",8.8,9.1,Cottage Treetop Villa,1 king bed,297.597,Ko Tao,Thailand,0,0,0,1,0,0,0,1
+Arize Hotel Sukhumvit,"Khlong Toei, Bangkok",7.4,0.0,Deluxe Double Room,1 king bed,148.604,Bangkok,Thailand,0,0,0,1,0,0,0,1
+AARIHA SUITES,Ao Nang Beach,6.9,0.0,Deluxe Double Room with Balcony,1 king bed,40.230000000000004,Krabi,Thailand,0,0,0,1,0,0,0,1
+Grace Hotel,"Wattana, Bangkok",6.5,0.0,Superior Double or Twin Room,Multiple bed types,106.876,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Baan Hin Sai Resort & Spa - SHA Extra Plus,Chaweng Noi Beach,8.3,8.2,Standard Double or Twin Room,   1 double or 2 twins,116.388,Chaweng,Thailand,2,0,0,0,0,0,0,2
+Papillon Resort,"Chaweng Beachfront, Chaweng",8.5,8.6,Superior Bungalow,1 queen bed,92.32900000000001,Chaweng,Thailand,0,0,1,0,0,0,0,1
+Lanna Palace 2004,"Chang Khlan, Chiang Mai",6.1,0.0,Standard Twin Room,2 twin beds,55.129,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Santhiya Koh Yao Yai Resort & Spa,Ko Yao Yai,8.6,8.9,Plus Round Trip Join Speedboat As Scheduled Transfer - Supreme Deluxe Sea View,1 queen bed,292.272,Ko Yao Yai,Thailand,0,0,1,0,0,0,0,1
+SR Sea View Apartments,Patong Beach,6.8,0.0,Deluxe Double Room with Sea View,1 queen bed,46.189,Phuket,Thailand,0,0,1,0,0,0,0,1
+Grand Orlov,Patong Beach,8.0,0.0,Standard King Room,1 queen bed,75.81,Phuket,Thailand,0,0,1,0,0,0,0,1
+Samed Cliff Resort,"Ao Noi Nha, Ko Samed",6.8,0.0,Standard Double Room,1 king bed,186.322,Ko Samed,Thailand,0,0,0,1,0,0,0,1
+Lanta Amara Resort,"Long Beach, Ko Lanta",9.4,9.7,Large Double Room,1 king bed,95.262,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Nasa Bangkok - SHA PLUS Certified,Bangkok,7.9,8.1,Standard Double or Twin Room,   1 double or 2 twins,52.079,Bangkok,Thailand,2,0,0,0,0,0,0,2
+"Nature Beach Resort, Koh Lanta","Klong Nin Beach, Ko Lanta",8.4,8.3,Double Room with Fan,1 full bed,26.201,Ko Lanta,Thailand,0,1,0,0,0,0,0,1
+Blue View House Phi Phi,Phi Phi Islands,8.1,8.0,Double Room with Balcony,1 queen bed,89.279,Phi Phi Islands,Thailand,0,0,1,0,0,0,0,1
+Villa Navin,Jomtien Beach,6.7,0.0,Deluxe Studio,1 queen bed,66.959,Pattaya,Thailand,0,0,1,0,0,0,0,1
+The Malika Hotel - SHA Extra Plus,Phuket,8.7,9.2,Room with City View,1 queen bed,97.042,Phuket,Thailand,0,0,1,0,0,0,0,1
+Forra Dive Resort Pattaya Beach,"Ko Lipe Pattaya Beach, Ko Lipe",6.9,0.0,Standard Bungalow with Garden View,1 queen bed,60.998000000000005,Ko Lipe,Thailand,0,0,1,0,0,0,0,1
+Chanchalay Hip Hostel SHA Extra Plus,Krabi,9.1,9.2,Double or Twin room with shared bathroom (Air conditioning),   1 double or 2 twins,32.902,Krabi,Thailand,2,0,0,0,0,0,0,2
+The Ozone Krabi Condotel,Krabi,8.3,8.4,Triple Room with Mountain View,1 full bed,72.181,Krabi,Thailand,0,1,0,0,0,0,0,1
+Wixky hotel,Nong Khai,7.9,0.0,Deluxe Queen Room,1 queen bed,64.048,Nong Khai,Thailand,0,0,1,0,0,0,0,1
+Andaman Pearl Resort,Ao Nang Beach,8.5,8.7,Deluxe Room,   1 double or 2 twins,82.153,Krabi,Thailand,2,0,0,0,0,0,0,2
+Cliff Cottage,"Bang Bao Bay, Ko Chang",8.7,8.2,Tent- Mongolian Tent for 2 Adults,2 twin beds,58.225,Ko Chang,Thailand,2,0,0,0,0,0,0,2
+Phuket Beach Glamping,Rawai Beach,7.8,0.0,Geo Tent,1 queen bed,295.702,Phuket,Thailand,0,0,1,0,0,0,0,1
+Bansaeo Garden and Resort,Chiang Saen,8.8,9.2,Deluxe Double Room with Bath,1 queen bed,129.39000000000001,Chiang Rai,Thailand,0,0,1,0,0,0,0,1
+Elixir Koh Yao Yai - SHA Plus,Ko Yao Yai,8.8,8.9,Superior Villa,1 full bed,168.386,Ko Yao Yai,Thailand,0,1,0,0,0,0,0,1
+Momento Resort,Pattaya South,7.1,0.0,Double Room with Private Bathroom,1 king bed,42.052,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Sun Inn,Songkhla,8.4,8.9,Standard Queen Room,1 queen bed,48.393,Songkhla,Thailand,0,0,1,0,0,0,0,1
+The Patong Center Hotel-SHA Certified,Patong Beach,7.3,0.0,Deluxe Double Room,1 full bed,49.11,Phuket,Thailand,0,1,0,0,0,0,0,1
+The Chanthong Residence and Hotel Pattaya,Pattaya,8.3,8.4,Standard Double Bed Room with Street View,1 king bed,41.806,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Koh Phaluai Beach Bangalow,Ban Ko Nok Taphao,9.1,8.3,Bungalow with Garden View,1 full bed,92.19,Not specified,Thailand,0,1,0,0,0,0,0,1
+Krabi River Hotel,Krabi,8.5,8.5,Twin Room with Sea View,2 twin beds,63.469,Krabi,Thailand,2,0,0,0,0,0,0,2
+Atlantis Condo Resort jomtien,Jomtien Beach,6.6,0.0,Standard Apartment,"2 beds (1 king, 1 sofa bed)",70.938,Pattaya,Thailand,0,0,0,1,0,0,0,2
+Kata Max Luxe1,Ban Karon,0.0,0.0,Two-Bedroom Villa,"3 beds (1 sofa bed, 2 queens)",42.773,Phuket,Thailand,0,0,2,0,0,0,0,3
+"The Hideaway, Koh Lipe","Ko Lipe Sunrise Beach, Ko Lipe",7.9,0.0,Double Room with Garden View,1 queen bed,3765.4030000000002,Ko Lipe,Thailand,0,0,1,0,0,0,0,1
+Baan Chaylay Resort Karon,Karon Beach,7.9,8.1,Standard Double Room,1 king bed,40.296,Phuket,Thailand,0,0,0,1,0,0,0,1
+Cocoo-n Beach Hotel,Patong Beach,6.1,0.0,Standard King Room,1 king bed,28.071,Phuket,Thailand,0,0,0,1,0,0,0,1
+Chom Suan Homestay,"Klong Chao Beach, Ko Kood",7.0,0.0,Deluxe Double Room,1 full bed,64.69500000000001,Ko Kood,Thailand,0,1,0,0,0,0,0,1
+Hotel Scarlett,Ban Ba Ngan,9.1,9.6,Superior Double Room,1 king bed,80.869,Not specified,Thailand,0,0,0,1,0,0,0,1
+Phranang Place- SHA Extra,Ao Nang Beach,8.2,8.4,Superior Double Room,   1 double or 2 twins,66.543,Krabi,Thailand,2,0,0,0,0,0,0,2
+Tropical Garden Bungalow,Phi Phi Islands,6.8,0.0,Fan Bungalow Twin Bed,2 twin beds,52.403,Phi Phi Islands,Thailand,2,0,0,0,0,0,0,2
+Easy Life Bungalows,Haad Yao,7.2,0.0,Superior Bungalow,1 full bed,35.582,Ko Phangan,Thailand,0,1,0,0,0,0,0,1
+White Orchid Inn II,"Khlong Toei, Bangkok",5.2,0.0,Superior Double Room,1 full bed,40.758,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Rawai Whale Resort,Rawai Beach,8.1,8.2,King Studio,1 queen bed,50.277,Phuket,Thailand,0,0,1,0,0,0,0,1
+NN Apartment,Pattaya South,8.6,9.0,Deluxe Double Room,1 king bed,37.847,Pattaya,Thailand,0,0,0,1,0,0,0,1
+7 Days Premium Hotel Pattaya,"Pattaya Walking Street, Pattaya South",7.4,0.0,Deluxe Double or Twin Room with City View,   1 double or 2 twins,69.172,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Grand Mansion Hotel,Krabi,7.5,8.0,Standard Double or Twin Room with Air Conditioning,   1 double or 2 twins,36.876,Krabi,Thailand,2,0,0,0,0,0,0,2
+VX The Fifty,"Khlong Toei, Bangkok",7.5,0.0,Bed in 4-Bed Dormitory Room,1 full bed,27.107,Bangkok,Thailand,0,1,0,0,0,0,0,1
+"Baan Suan Kayoo 2,   ",Ko Phayam,8.9,8.4,Bungalow with Fan,1 queen bed,30.116,Ko Phayam,Thailand,0,0,1,0,0,0,0,1
+Phi Phi Sand Sea View Resort,"Tonsai Bay, Phi Phi Islands",7.1,0.0,Standard Double Room (Building),1 queen bed,62.107,Phi Phi Islands,Thailand,0,0,1,0,0,0,0,1
+"Pailin Pool Villa, Hua Hin",Hua Hin,8.8,9.2,Superior Villa,1 queen bed,199.84300000000002,Hua Hin,Thailand,0,0,1,0,0,0,0,1
+Silver Resortel,Patong Beach,8.1,8.6,Standard King Room,1 king bed,54.159,Phuket,Thailand,0,0,0,1,0,0,0,1
+Veranda Lodge,Hua Hin,7.8,8.1,Garden Bungalow,1 king bed,242.05100000000002,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+Golden Nest Hotel Suvarnabhumi,Bangkok,8.0,8.5,Economy Double Room,1 king bed,83.22500000000001,Bangkok,Thailand,0,0,0,1,0,0,0,1
+JJ Beach Resort & JJ Seafood,"Aow Yai Beach, Ko Phayam",9.1,9.0,Deluxe Bungalow,1 queen bed,70.24,Ko Phayam,Thailand,0,0,1,0,0,0,0,1
+64House,"Chang Moi, Chiang Mai",8.2,8.2,Triple Room with Bath,"2 beds (1 twin, 1 queen)",78.581,Chiang Mai,Thailand,1,0,1,0,0,0,0,2
+Nam Talay Resort,Pran Buri,8.2,8.5,Standard Double Room,1 full bed,49.445,Prachuap Khiri Khan,Thailand,0,1,0,0,0,0,0,1
+Baan Suan Rim Klong,"Klong Khong Beach, Ko Lanta",9.0,9.1,Superior Double Room,1 king bed,49.882,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+O'nya Phuket Hotel - SHA Extra Plus,Phuket,8.5,9.1,Superior Double or Twin Room,"3 beds (2 twins, 1 queen)",59.65,Phuket,Thailand,2,0,1,0,0,0,0,3
+The Loft Panwa Resort,Panwa Beach,8.8,8.9,Standard Double or Twin Room with Garden View,Multiple bed types,93.327,Phuket,Thailand,0,0,0,0,0,0,0,0
+Sugar Marina Resort - ART - Karon Beach - SHA Plus,Karon Beach,8.2,8.4,Deluxe Double or Twin Room,Multiple bed types,108.659,Phuket,Thailand,0,0,0,0,0,0,0,0
+Park 38 Hotel - SHA Plus,Phuket,7.9,0.0,Superior Twin Room,2 twin beds,74.238,Phuket,Thailand,2,0,0,0,0,0,0,2
+Erawan House,"Khaosan, Bangkok",7.5,0.0,Superior Double Room,1 full bed,67.93,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Rocco Hua Hin Beach Seaview,Hua Hin,7.5,0.0,Studio with Sofa Bed,"2 beds (1 sofa bed, 1 queen)",139.738,Hua Hin,Thailand,0,0,1,0,0,0,0,2
+Has Pattaya,Pattaya South,8.2,8.8,Superior Double or Twin Room with City View,1 king bed,110.628,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Madina Rayong Hotel,Rayong,7.6,8.0,Standard Twin Room,2 twin beds,109.33500000000001,Rayong,Thailand,2,0,0,0,0,0,0,2
+Saisawan Beach Resort,Na Jomtien,7.8,8.4,Deluxe Room with City View,   1 double or 2 twins,105.037,Pattaya,Thailand,2,0,0,0,0,0,0,2
+,Ban Don Rak,8.6,9.0,Double Room with Private Bathroom,1 king bed,35.628,Not specified,Thailand,0,0,0,1,0,0,0,1
+Jaroonwej Bangsaen,Bangsaen,5.8,0.0,Standard Apartment,1 king bed,58.435,Chon Buri,Thailand,0,0,0,1,0,0,0,1
+P'Un House,"Sairee Beach, Ko Tao",7.6,0.0,Standard Double Room with Air-Conditioning,1 full bed,48.554,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Srisomthai Hotel,Ubon Ratchathani,7.8,8.1,Standard Double Room,1 full bed,32.994,Ubon Ratchathani,Thailand,0,1,0,0,0,0,0,1
+Andaman Bay Bungalow,"Klong Khong Beach, Ko Lanta",7.1,0.0,Budget Double Room,1 full bed,22.643,Ko Lanta,Thailand,0,1,0,0,0,0,0,1
+President Hotel Udonthani,Udon Thani,8.1,8.4,Deluxe Double Room,1 queen bed,60.49,Udon Thani,Thailand,0,0,1,0,0,0,0,1
+,Sangkhla Buri,8.0,8.4,Double Room,1 king bed,61.137,Kanchanaburi,Thailand,0,0,0,1,0,0,0,1
+Phalarn Inn Resort,Mae Nam,7.5,0.0,Two-Bedroom Cottage,"2 beds (1 full, 1 queen)",116.45100000000001,Ko Samui,Thailand,0,1,1,0,0,0,0,2
+Poseidon Bungalows,Khao Lak,7.9,0.0,Bungalow with Sea View,"3 beds (1 full, 2 bunk beds)",94.886,Phang Nga,Thailand,0,1,0,0,0,2,0,3
+65 hostel chiangmai,Chiang Mai,6.7,0.0,Double or Twin Room with Bathroom,1 full bed,32.347,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+Diary Suite,Nakhon Pathom,8.5,8.8,Deluxe Twin Room,2 twin beds,54.991,Nakhon Pathom,Thailand,2,0,0,0,0,0,0,2
+Shanti Lodge Phuket,Chalong,7.8,0.0,Double Room with Shared Bathroom,"3 beds (2 twins, 1 queen)",49.556000000000004,Phuket,Thailand,2,0,1,0,0,0,0,3
+ ? (Once upon a time),"Chiang Khan Walking Street, Chiang Khan",7.5,8.0,Double Room with Shared Bathroom,1 king bed,32.994,Chiang Khan,Thailand,0,0,0,1,0,0,0,1
+Rasta view Bangalows,Ban Khlong Kloi,7.4,0.0,Double Room,1 full bed,29.113,Not specified,Thailand,0,1,0,0,0,0,0,1
+Garden Corner Resort & Hotel,Phitsanulok,7.4,0.0,Standard King Room,1 king bed,38.817,Phitsanulok,Thailand,0,0,0,1,0,0,0,1
+D5 Hotel  ,Chon Buri,8.7,8.8,Deluxe Double Room,1 queen bed,32.445,Chon Buri,Thailand,0,0,1,0,0,0,0,1
+U Hostel Haad Rin,Ko Phangan,8.0,0.0,Economy Quadruple Room,2 bunk beds,113.986,Ko Phangan,Thailand,0,0,0,0,0,2,0,2
+No. 8 Pai,Pai,8.4,8.6,Standard Double Room with Fan,1 full bed,32.347,Mae Hong Son,Thailand,0,1,0,0,0,0,0,1
+Norn Talay Surin Beach Phuket,Surin Beach,7.9,8.1,Deluxe Double or Twin Room with Sea View,"2 beds (1 sofa bed, 1 queen)",344.861,Phuket,Thailand,0,0,1,0,0,0,0,2
+Oriole Residence - Suvarnabhumi,Ban Khlong Thewa,8.8,9.2,Standard King Room,1 king bed,90.57300000000001,Not specified,Thailand,0,0,0,1,0,0,0,1
+   Impact,Thung Si Kan,7.4,0.0,Standard Twin Room,2 twin beds,58.919000000000004,Not specified,Thailand,2,0,0,0,0,0,0,2
+"Holiday Style Ao Nang Beach Resort, Krabi","Nopparat Thara Beach, Ao Nang Beach",8.2,8.5,Superior Double or Twin Room with Garden View,1 full bed,102.20400000000001,Krabi,Thailand,0,1,0,0,0,0,0,1
+Escape Beach Resort - SHA Extra Plus Certified,Mae Nam,8.2,8.6,Economy Double Room,1 king bed,119.778,Ko Samui,Thailand,0,0,0,1,0,0,0,1
+Avani Plus Riverside Bangkok Hotel,"Thonburi, Bangkok",9.0,9.4,Avani River View Two Bedroom Suite,"4 beds (2 twins, 1 king, 1 sofa bed)",1724.635,Bangkok,Thailand,2,0,0,1,0,0,0,4
+VELA Dhi Udon Thani,Udon Thani,8.9,9.4,Deluxe Twin Room,2 twin beds,145.564,Udon Thani,Thailand,2,0,0,0,0,0,0,2
+Anahata Resort - SHA Plus,Lipa Noi,8.9,9.1,Superior Bungalow,1 full bed,178.558,Ko Samui,Thailand,0,1,0,0,0,0,0,1
+Outrigger Surin Beach Resort - SHA Extra Plus,Surin Beach,9.1,9.5,Grand Surin Suite 1 King,1 king bed,548.629,Phuket,Thailand,0,0,0,1,0,0,0,1
+Yao Yai Beach Resort,Ko Yao Yai,8.5,8.8,Standard Room - No Sea View,1 queen bed,152.717,Ko Yao Yai,Thailand,0,0,1,0,0,0,0,1
+The Peri Hotel Khao Yai,Phayayen,8.0,8.4,King Room with Mountain View,1 king bed,197.905,Nakhon Ratchasima,Thailand,0,0,0,1,0,0,0,1
+Khao Sok Jungle Huts Resort,Khao Sok National Park,8.2,8.1,Bungalow with Air-Conditioner,1 full bed,65.989,Surat Thani,Thailand,0,1,0,0,0,0,0,1
+The Sunnery Ville,Lom Kao,8.4,8.7,King Room with Mountain View,1 king bed,102.86500000000001,Phetchabun,Thailand,0,0,0,1,0,0,0,1
+Anana Ecological Resort Krabi-SHA Extra Plus,Ao Nang Beach,8.9,9.2,Superior Double or Twin Room,1 king bed,206.895,Krabi,Thailand,0,0,0,1,0,0,0,1
+Hotel Ibis Samui Bophut,Bophut,7.6,8.0,Standard Twin Room,2 twin beds,93.885,Ko Samui,Thailand,2,0,0,0,0,0,0,2
+VST Residence -SHA PLUS Certified,Ban Khlong Bang Ping,8.4,8.8,Superior Twin Room,2 twin beds,80.22200000000001,Not specified,Thailand,2,0,0,0,0,0,0,2
+Na Siri Lake View,Samut Prakan,7.6,8.0,Apartment with Garden View,2 twin beds,76.392,Samut Prakan,Thailand,2,0,0,0,0,0,0,2
+Hivetel,Chalong,7.8,8.2,Standard Double Room,1 king bed,84.23,Phuket,Thailand,0,0,0,1,0,0,0,1
+Hotel Tide Phuket Beach front - SHA Plus,Phuket,8.4,9.0,Deluxe Room,1 queen bed,192.737,Phuket,Thailand,0,0,1,0,0,0,0,1
+Amatara Welleisure Resort - SHA Extra Plus,Panwa Beach,8.6,8.9,Cape Panwa Suite,1 king bed,510.887,Phuket,Thailand,0,0,0,1,0,0,0,1
+Style Paidoi Resort,Ban Pa Yang (3),9.6,9.6,Tent,1 full bed,74.78,Not specified,Thailand,0,1,0,0,0,0,0,1
+The Heritage Chiang Rai Hotel and Convention - SHA Extra Plus,Chiang Rai,8.7,9.1,Deluxe Double Room,1 king bed,109.504,Chiang Rai,Thailand,0,0,0,1,0,0,0,1
+Koh Tao Relax Freedom Beach Resort,"Chalok Baan Kao, Ko Tao",9.0,9.1,Superior House - Hillside (Free pick up and drop off at Mae Haad Pier),1 king bed,153.882,Ko Tao,Thailand,0,0,0,1,0,0,0,1
+@T Boutique Hotel,Klong Wan,8.2,8.4,Family Room,"3 beds (1 full, 2 bunk beds)",238.797,Prachuap Khiri Khan,Thailand,0,1,0,0,0,2,0,3
+Rayong Marriott Resort & Spa,Klaeng,7.9,8.3,Deluxe King Room with Mountain View,"2 beds (1 full, 1 king)",316.862,Rayong,Thailand,0,1,0,1,0,0,0,2
+Tiki Beach Koh Phangan,Ban Tai,8.0,0.0,Deluxe Bungalow with Garden View,"2 beds (1 twin, 1 full)",166.733,Ko Phangan,Thailand,1,1,0,0,0,0,0,2
+Coconut Beach Bungalows,Chaloklum,9.3,9.2,Two-Bedroom Bungalow,2 queen beds,339.875,Ko Phangan,Thailand,0,0,2,0,0,0,0,2
+Venice Krabi Villa Resort- SHA Plus,Ao Nam Mao,8.9,9.6,Lagoon Grand Deluxe Twin,2 twin beds,203.026,Krabi,Thailand,2,0,0,0,0,0,0,2
+O2 Hotel Lopburi,Lop Buri,7.6,8.2,Superior King Room,1 king bed,54.991,Lop Buri,Thailand,0,0,0,1,0,0,0,1
+Hotel Agnes,Buriram,8.8,9.2,Large Twin Room,Multiple bed types,93.161,Buriram,Thailand,0,0,0,0,0,0,0,0
+The Dorm Hostel,Ban Lo Long,8.7,8.9,Bunk Bed in Mixed Dormitory Room,1 queen bed,40.269,Not specified,Thailand,0,0,1,0,0,0,0,1
+Somerset Pattaya - SHA Plus,Pattaya,8.9,9.3,Deluxe Twin Room,2 twin beds,323.004,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Inpawa Hotel,Ban Phai,8.2,8.6,Superior Double Room,1 queen bed,77.634,Khon Kaen,Thailand,0,0,1,0,0,0,0,1
+Chiangkhong Teak Garden Riverfront Onsen Hotel- SHA Extra Plus,Chiang Khong,8.3,8.7,Resort Garden View,Multiple bed types,87.809,Chiang Rai,Thailand,0,0,0,0,0,0,0,0
+Island Escape by Burasari - SHA Extra Plus,Phuket,8.5,9.2,Deluxe Room with Schedule Boat Transfer,   1 double or 2 twins,318.817,Phuket,Thailand,2,0,0,0,0,0,0,2
+  Double D hotel,Pak Kret,8.6,8.9,Deluxe King Room,1 king bed,86.756,Nonthaburi,Thailand,0,0,0,1,0,0,0,1
+"Tawaravadee Resort, BW Signature Collection","304 Industrial Park, Si Maha Phot",8.2,8.8,Superior Double Room,1 full bed,88.52,Prachin Buri,Thailand,0,1,0,0,0,0,0,1
+Lub d Phuket Patong,Patong Beach,8.1,8.4,Junior Twin Room,2 twin beds,85.926,Phuket,Thailand,2,0,0,0,0,0,0,2
+The Waters Khao Lak by Katathani - SHA Extra Plus,"Bang Niang Beach, Khao Lak",8.7,9.0,Upper Pool Access Room,   1 double or 2 twins,157.856,Phang Nga,Thailand,2,0,0,0,0,0,0,2
+VILLA DEVA RESORT AND HOTEL,"Sathorn, Thung Maha Mek",9.7,9.7,Deluxe Twin Pool View,2 twin beds,511.09000000000003,Bangkok,Thailand,2,0,0,0,0,0,0,2
+VILLA DEVA RESORT AND HOTEL,"Sathorn, Thung Maha Mek",9.7,9.7,Deluxe Twin Pool View,2 twin beds,511.09000000000003,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Panviman Chiang Mai Spa Resort,Mae Rim,8.7,8.9,Valley Deluxe Double Room,1 king bed,191.486,Mae Rim,Thailand,0,0,0,1,0,0,0,1
+Panviman Chiang Mai Spa Resort,Mae Rim,8.7,8.9,Valley Deluxe Double Room,1 king bed,191.486,Mae Rim,Thailand,0,0,0,1,0,0,0,1
+Napa Hotel Ratchaburi,Ratchaburi,8.5,8.8,Superior Twin Room,2 twin beds,78.558,Ratchaburi,Thailand,2,0,0,0,0,0,0,2
+Napa Hotel Ratchaburi,Ratchaburi,8.5,8.8,Superior Twin Room,2 twin beds,78.558,Ratchaburi,Thailand,2,0,0,0,0,0,0,2
+Centre Point Hotel Terminal21 Korat,Nakhon Ratchasima,8.9,9.4,Deluxe King Room 2 Pax,1 king bed,124.492,Nakhon Ratchasima,Thailand,0,0,0,1,0,0,0,1
+Centre Point Hotel Terminal21 Korat,Nakhon Ratchasima,8.9,9.4,Deluxe King Room 2 Pax,1 king bed,124.492,Nakhon Ratchasima,Thailand,0,0,0,1,0,0,0,1
+Fortune River View Hotel Nakhon Phanom,Nakhon Phanom,8.2,8.6,Deluxe River View Room,2 twin beds,153.903,Nakhon Phanom,Thailand,2,0,0,0,0,0,0,2
+Fortune River View Hotel Nakhon Phanom,Nakhon Phanom,8.2,8.6,Deluxe River View Room,2 twin beds,153.903,Nakhon Phanom,Thailand,2,0,0,0,0,0,0,2
+Fortune Riverview Hotel Chiang Khong,Chiang Khong,8.0,8.5,Superior Double or Twin Room with Pool View,Multiple bed types,113.11800000000001,Chiang Rai,Thailand,0,0,0,0,0,0,0,0
+"Holiday Inn Resort Samui Bophut Beach, an IHG Hotel","Fisherman Village, Bophut",8.7,9.2,Standard King Room with Balcony,1 king bed,245.388,Bophut,Thailand,0,0,0,1,0,0,0,1
+"Holiday Inn Resort Samui Bophut Beach, an IHG Hotel","Fisherman Village, Bophut",8.7,9.2,Standard King Room with Balcony,1 king bed,245.388,Bophut,Thailand,0,0,0,1,0,0,0,1
+Outrigger Khao Lak Beach Resort - SHA Extra Plus,"Bangsak Beach, Khao Lak",9.2,9.6,Deluxe Garden Balcony - King Bed,1 king bed,173.243,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+Outrigger Khao Lak Beach Resort - SHA Extra Plus,"Bangsak Beach, Khao Lak",9.2,9.6,Deluxe Garden Balcony - King Bed,1 king bed,173.243,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+Outrigger Koh Samui Beach Resort - SHA Extra Plus,Lamai,8.5,9.1,Superior Balcony -King Bed,1 king bed,225.835,Lamai,Thailand,0,0,0,1,0,0,0,1
+Outrigger Koh Samui Beach Resort - SHA Extra Plus,Lamai,8.5,9.1,Superior Balcony -King Bed,1 king bed,225.835,Lamai,Thailand,0,0,0,1,0,0,0,1
+Katiliya Mountain Resort And Spa,Mae Salong Nai,8.7,9.0,Standard Suite,1 king bed,285.952,Mae Salong Nai,Thailand,0,0,0,1,0,0,0,1
+Katiliya Mountain Resort And Spa,Mae Salong Nai,8.7,9.0,Standard Suite,1 king bed,285.952,Mae Salong Nai,Thailand,0,0,0,1,0,0,0,1
+Grand Inter Hotel,Samut Sakhon,8.2,8.4,Deluxe Double Room with Balcony,1 king bed,57.084,Samut Sakhon,Thailand,0,0,0,1,0,0,0,1
+Grand Inter Hotel,Samut Sakhon,8.2,8.4,Deluxe Double Room with Balcony,1 king bed,57.084,Samut Sakhon,Thailand,0,0,0,1,0,0,0,1
+JonoX Phuket Karon Hotel,Karon Beach,8.6,8.7,Recharge,Multiple bed types,143.756,Phuket,Thailand,0,0,0,0,0,0,0,0
+Scandia Beach Hotel,Jomtien Beach,7.3,0.0,Superior Twin Room with Balcony,2 twin beds,39.771,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Come Moon Loft Hotel,Phrae,8.5,8.8,Double Room,1 full bed,45.998,Phrae,Thailand,0,1,0,0,0,0,0,1
+Come Moon Loft Hotel,Phrae,8.5,8.8,Double Room,1 full bed,45.998,Phrae,Thailand,0,1,0,0,0,0,0,1
+Lady Naya Villas - SHA Extra Plus,Rawai Beach,9.1,9.3,Deluxe Double or Twin Room with Pool Access,Multiple bed types,135.859,Phuket,Thailand,0,0,0,0,0,0,0,0
+Fortune View Khong Hotel Nakhon Phanom,Nakhon Phanom,8.4,8.8,Superior Double Room with City View,1 full bed,95.793,Nakhon Phanom,Thailand,0,1,0,0,0,0,0,1
+Fortune View Khong Hotel Nakhon Phanom,Nakhon Phanom,8.4,8.8,Superior Double Room with City View,1 full bed,95.793,Nakhon Phanom,Thailand,0,1,0,0,0,0,0,1
+Dusit Thani Krabi Beach Resort - SHA Extra Plus,Klong Muang Beach,8.7,9.1,Deluxe King Room,1 king bed,368.524,Klong Muang Beach,Thailand,0,0,0,1,0,0,0,1
+Dusit Thani Krabi Beach Resort - SHA Extra Plus,Klong Muang Beach,8.7,9.1,Deluxe King Room,1 king bed,368.524,Klong Muang Beach,Thailand,0,0,0,1,0,0,0,1
+Centra by Centara Hotel Mae Sot,Mae Sot,7.7,8.3,Superior Twin Room,2 twin beds,63.237,Mae Sot,Thailand,2,0,0,0,0,0,0,2
+Centra by Centara Hotel Mae Sot,Mae Sot,7.7,8.3,Superior Twin Room,2 twin beds,63.237,Mae Sot,Thailand,2,0,0,0,0,0,0,2
+The Oceanic Sportel - SHA Extra Plus,Phuket,8.2,9.2,Deluxe King Room,1 king bed,114.926,Phuket,Thailand,0,0,0,1,0,0,0,1
+Novotel Marina Sriracha & Koh Si Chang,Si Racha,8.5,9.1,"Premiere, Deluxe Room, Lounge Access, Sea View - 1 King Bed Sea View and Lounge Access",1 king bed,233.25300000000001,Si Racha,Thailand,0,0,0,1,0,0,0,1
+Novotel Marina Sriracha & Koh Si Chang,Si Racha,8.5,9.1,"Premiere, Deluxe Room, Lounge Access, Sea View - 1 King Bed Sea View and Lounge Access",1 king bed,233.25300000000001,Si Racha,Thailand,0,0,0,1,0,0,0,1
+3,Ban Ko Mu (1),8.0,10.0,Budget Double Room,1 full bed,32.994,Not specified,Thailand,0,1,0,0,0,0,0,1
+3,Ban Ko Mu (1),8.0,10.0,Budget Double Room,1 full bed,32.994,Not specified,Thailand,0,1,0,0,0,0,0,1
+Riverfront Hotel Mukdahan,Mukdahan,7.8,8.1,Standard Double Room,1 queen bed,66.959,Mukdahan,Thailand,0,0,1,0,0,0,0,1
+Riverfront Hotel Mukdahan,Mukdahan,7.8,8.1,Standard Double Room,1 queen bed,66.959,Mukdahan,Thailand,0,0,1,0,0,0,0,1
+The Journey Hotel Bangna,Samut Prakan,8.7,9.1,Standard King Room,1 full bed,68.72500000000001,Samut Prakan,Thailand,0,1,0,0,0,0,0,1
+X10 Khaolak Resort SHA Plus,Khao Lak,8.9,9.2,Deluxe Double or Twin Room - Family Wing,   1 double or 2 twins,136.007,Phang Nga,Thailand,2,0,0,0,0,0,0,2
+Namaka Resort Kamala - SHA Extra Plus,Kamala Beach,7.8,8.3,Premier Room with Sea View,1 queen bed,217.41,Kamala Beach,Thailand,0,0,1,0,0,0,0,1
+Namaka Resort Kamala - SHA Extra Plus,Kamala Beach,7.8,8.3,Premier Room with Sea View,1 queen bed,217.41,Kamala Beach,Thailand,0,0,1,0,0,0,0,1
+SKYVIEW Hotel Bangkok - SHA Extra Plus,"Khlong Toei, Bangkok",8.6,9.2,Premier,Multiple bed types,360.687,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Diamond Resort Phuket - SHA,Bang Tao Beach,8.8,9.0,Deluxe Suite Double,"2 beds (1 full, 1 sofa bed)",222.84,Bang Tao Beach,Thailand,0,1,0,0,0,0,0,2
+Diamond Resort Phuket - SHA,Bang Tao Beach,8.8,9.0,Deluxe Suite Double,"2 beds (1 full, 1 sofa bed)",222.84,Bang Tao Beach,Thailand,0,1,0,0,0,0,0,2
+Blue Sky Residence Airport,Ban Bang Phli Yai,7.9,8.3,Superior Twin Room,2 twin beds,57.039,Ban Bang Phli Yai,Thailand,2,0,0,0,0,0,0,2
+Blue Sky Residence Airport,Ban Bang Phli Yai,7.9,8.3,Superior Twin Room,2 twin beds,57.039,Ban Bang Phli Yai,Thailand,2,0,0,0,0,0,0,2
+Sukantara Cascade Resort and Spa,Mae Rim,8.8,8.8,Deluxe Jacuzzi Panorama,1 queen bed,305.915,Mae Rim,Thailand,0,0,1,0,0,0,0,1
+Sukantara Cascade Resort and Spa,Mae Rim,8.8,8.8,Deluxe Jacuzzi Panorama,1 queen bed,305.915,Mae Rim,Thailand,0,0,1,0,0,0,0,1
+Huernnana Boutique Hotel,Phrae,7.0,0.0,Nana Deluxe Twin Room,2 twin beds,139.90800000000002,Phrae,Thailand,2,0,0,0,0,0,0,2
+Huernnana Boutique Hotel,Phrae,7.0,0.0,Nana Deluxe Twin Room,2 twin beds,139.90800000000002,Phrae,Thailand,2,0,0,0,0,0,0,2
+Amari Phuket,Patong Beach,8.2,8.4,One-Bedroom Suite,1 queen bed,610.76,Phuket,Thailand,0,0,1,0,0,0,0,1
+Paripas Patong Resort - SHA Extra Plus,Patong Beach,8.0,8.4,Deluxe Twin or Double Room with Balcony (No View),   1 double or 2 twins,116.399,Phuket,Thailand,2,0,0,0,0,0,0,2
+The Canal 304,"304 Industrial Park, Si Maha Phot",8.9,8.9,Luxury Suite,1 king bed,155.268,Prachin Buri,Thailand,0,0,0,1,0,0,0,1
+Mövenpick Resort Khao Yai,Ban Wang Sai,7.6,8.0,Deluxe King Room,1 full bed,260.327,Ban Wang Sai,Thailand,0,1,0,0,0,0,0,1
+Mövenpick Resort Khao Yai,Ban Wang Sai,7.6,8.0,Deluxe King Room,1 full bed,260.327,Ban Wang Sai,Thailand,0,1,0,0,0,0,0,1
+Phuphavaree by Tahug Sai Yok,Ban Ai Hit,7.3,0.0,Tent,1 queen bed,55.379,Ban Ai Hit,Thailand,0,0,1,0,0,0,0,1
+Phuphavaree by Tahug Sai Yok,Ban Ai Hit,7.3,0.0,Tent,1 queen bed,55.379,Ban Ai Hit,Thailand,0,0,1,0,0,0,0,1
+La Miniera Pool Villas Pattaya - SHA Plus,Nong Prue,8.8,9.4,Pool Villa,Multiple bed types,495.891,Nong Prue,Thailand,0,0,0,0,0,0,0,0
+La Miniera Pool Villas Pattaya - SHA Plus,Nong Prue,8.8,9.4,Pool Villa,Multiple bed types,495.891,Nong Prue,Thailand,0,0,0,0,0,0,0,0
+Phu Pha Phung Resort,Suan Phung,7.6,0.0,Villa Family,"9 beds (7 twins, 2 fulls)",438.216,Suan Phung,Thailand,7,2,0,0,0,0,0,9
+Phu Pha Phung Resort,Suan Phung,7.6,0.0,Villa Family,"9 beds (7 twins, 2 fulls)",438.216,Suan Phung,Thailand,7,2,0,0,0,0,0,9
+Limon Villa Khao Yai by Tolani,Wangkata,7.9,8.1,Deluxe Villa,1 king bed,132.92000000000002,Wangkata,Thailand,0,0,0,1,0,0,0,1
+Limon Villa Khao Yai by Tolani,Wangkata,7.9,8.1,Deluxe Villa,1 king bed,132.92000000000002,Wangkata,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok IMPACT,Nonthaburi,8.4,8.9,Executive King Room,1 king bed,223.185,Nonthaburi,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok IMPACT,Nonthaburi,8.4,8.9,Executive King Room,1 king bed,223.185,Nonthaburi,Thailand,0,0,0,1,0,0,0,1
+Samui Resotel Beach Resort - SHA extra plus,"Chaweng Beachfront, Chaweng",8.1,8.5,Standard Double Room,1 king bed,256.658,Chaweng,Thailand,0,0,0,1,0,0,0,1
+Anchalee Resort,Bang Saphan,7.8,0.0,Double Room,1 full bed,43.022,Bang Saphan,Thailand,0,1,0,0,0,0,0,1
+Anchalee Resort,Bang Saphan,7.8,0.0,Double Room,1 full bed,43.022,Bang Saphan,Thailand,0,1,0,0,0,0,0,1
+Regent Lodge Lampang,Lampang,7.8,8.1,Superior Double Room,1 full bed,29.113,Lampang,Thailand,0,1,0,0,0,0,0,1
+Regent Lodge Lampang,Lampang,7.8,8.1,Superior Double Room,1 full bed,29.113,Lampang,Thailand,0,1,0,0,0,0,0,1
+Atta Lakeside Resort Suite - SHA Plus Certified,Mu Si,8.7,8.9,One-Bedroom Suite,"2 beds (1 king, 1 sofa bed)",303.475,Mu Si,Thailand,0,0,0,1,0,0,0,2
+Atta Lakeside Resort Suite - SHA Plus Certified,Mu Si,8.7,8.9,One-Bedroom Suite,"2 beds (1 king, 1 sofa bed)",303.475,Mu Si,Thailand,0,0,0,1,0,0,0,2
+"Kimpton Maa-Lai Bangkok, an IHG Hotel","Pathumwan, Bangkok",9.4,9.7,Essential Room,Multiple bed types,628.313,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Eastin Thana City Golf Resort Bangkok,Samutprakarn,8.4,8.9,Superior Room,2 twin beds,103.19200000000001,Samutprakarn,Thailand,2,0,0,0,0,0,0,2
+Eastin Thana City Golf Resort Bangkok,Samutprakarn,8.4,8.9,Superior Room,2 twin beds,103.19200000000001,Samutprakarn,Thailand,2,0,0,0,0,0,0,2
+Sukhothai Heritage Resort - SHA PLUS,Sawankhalok,8.2,8.5,Superior King Room with Pool View,1 king bed,142.774,Sawankhalok,Thailand,0,0,0,1,0,0,0,1
+Sukhothai Heritage Resort - SHA PLUS,Sawankhalok,8.2,8.5,Superior King Room with Pool View,1 king bed,142.774,Sawankhalok,Thailand,0,0,0,1,0,0,0,1
+El Matcha Lanta Resort,Phra Ae beach,8.9,9.3,Bungalow with Garden View,1 queen bed,93.9,Phra Ae beach,Thailand,0,0,1,0,0,0,0,1
+El Matcha Lanta Resort,Phra Ae beach,8.9,9.3,Bungalow with Garden View,1 queen bed,93.9,Phra Ae beach,Thailand,0,0,1,0,0,0,0,1
+Koh Yao Yai Village - SHA Extra Plus,Ko Yao Yai,8.9,9.0,Deluxe Villa,1 king bed,210.463,Ko Yao Yai,Thailand,0,0,0,1,0,0,0,1
+Pen Ta Hug Hotel,Ubon Ratchathani,8.0,8.2,Superior Double Room,1 queen bed,57.579,Ubon Ratchathani,Thailand,0,0,1,0,0,0,0,1
+Panviman Resort Koh Phangan - SHA Extra Plus,Thong Nai Pan Noi,8.6,8.8,Executive Suite,"2 beds (1 sofa bed, 1 queen)",2018.4840000000002,Thong Nai Pan Noi,Thailand,0,0,1,0,0,0,0,2
+Panviman Resort Koh Phangan - SHA Extra Plus,Thong Nai Pan Noi,8.6,8.8,Executive Suite,"2 beds (1 sofa bed, 1 queen)",2018.4840000000002,Thong Nai Pan Noi,Thailand,0,0,1,0,0,0,0,2
+The X10 Nordic Tent and Glamping Pool Villa Khaoyai  - SHA Certified,Ban Thung Sawang,8.0,8.3,Villa with Private Pool,1 king bed,271.084,Ban Thung Sawang,Thailand,0,0,0,1,0,0,0,1
+The X10 Nordic Tent and Glamping Pool Villa Khaoyai  - SHA Certified,Ban Thung Sawang,8.0,8.3,Villa with Private Pool,1 king bed,271.084,Ban Thung Sawang,Thailand,0,0,0,1,0,0,0,1
+THE WOOD,Surin,8.4,8.8,Standard Double Room,1 king bed,80.869,Surin,Thailand,0,0,0,1,0,0,0,1
+THE WOOD,Surin,8.4,8.8,Standard Double Room,1 king bed,80.869,Surin,Thailand,0,0,0,1,0,0,0,1
+Karon Phunaka Resort,Karon Beach,8.2,8.5,Superior Double or Twin Room with Sea View,   1 double or 2 twins,138.124,Phuket,Thailand,2,0,0,0,0,0,0,2
+The Sun resort Ratchaburi,Ratchaburi,8.4,8.5,Deluxe King Room,1 king bed,36.391,Ratchaburi,Thailand,0,0,0,1,0,0,0,1
+The Sun resort Ratchaburi,Ratchaburi,8.4,8.5,Deluxe King Room,1 king bed,36.391,Ratchaburi,Thailand,0,0,0,1,0,0,0,1
+Bella Villa Cabana,Na Kluea,7.5,8.0,Junior Suite with Pool View,"2 beds (1 twin, 1 full)",200.46200000000002,Na Kluea,Thailand,1,1,0,0,0,0,0,2
+Bella Villa Cabana,Na Kluea,7.5,8.0,Junior Suite with Pool View,"2 beds (1 twin, 1 full)",200.46200000000002,Na Kluea,Thailand,1,1,0,0,0,0,0,2
+Dolphin Bay Resort SHA-EXTRA PLUS HOTEL,Sam Roi Yot,8.2,8.2,Deluxe Double or Twin Room with Balcony,Multiple bed types,153.571,Sam Roi Yot,Thailand,0,0,0,0,0,0,0,0
+Dolphin Bay Resort SHA-EXTRA PLUS HOTEL,Sam Roi Yot,8.2,8.2,Deluxe Double or Twin Room with Balcony,Multiple bed types,153.571,Sam Roi Yot,Thailand,0,0,0,0,0,0,0,0
+KT Knight Hotel,Surin,8.8,9.0,Deluxe Twin Room,"2 beds (1 twin, 1 full)",48.521,Surin,Thailand,1,1,0,0,0,0,0,2
+KT Knight Hotel,Surin,8.8,9.0,Deluxe Twin Room,"2 beds (1 twin, 1 full)",48.521,Surin,Thailand,1,1,0,0,0,0,0,2
+Raya Buri Resort Kanchanaburi,Si Sawat,7.1,0.0,Hillside Superior Room,"2 beds (1 twin, 1 full)",141.1,Si Sawat,Thailand,1,1,0,0,0,0,0,2
+Raya Buri Resort Kanchanaburi,Si Sawat,7.1,0.0,Hillside Superior Room,"2 beds (1 twin, 1 full)",141.1,Si Sawat,Thailand,1,1,0,0,0,0,0,2
+Destination Resorts Phuket Karon Beach - SHA Extra Plus,Karon Beach,7.3,0.0,Standard King Room,1 queen bed,209.612,Phuket,Thailand,0,0,1,0,0,0,0,1
+Pamookkoo,Kata Beach,8.1,8.2,Deluxe Premium Room,1 king bed,291.127,Kata Beach,Thailand,0,0,0,1,0,0,0,1
+Pamookkoo,Kata Beach,8.1,8.2,Deluxe Premium Room,1 king bed,291.127,Kata Beach,Thailand,0,0,0,1,0,0,0,1
+Taris Art Hotel Phrae,Phrae,7.5,0.0,Superior Double Room,1 full bed,51.879,Phrae,Thailand,0,1,0,0,0,0,0,1
+Taris Art Hotel Phrae,Phrae,7.5,0.0,Superior Double Room,1 full bed,51.879,Phrae,Thailand,0,1,0,0,0,0,0,1
+Happy Home,Ratchaburi,8.4,8.7,Deluxe Double Room,1 full bed,41.922000000000004,Ratchaburi,Thailand,0,1,0,0,0,0,0,1
+Happy Home,Ratchaburi,8.4,8.7,Deluxe Double Room,1 full bed,41.922000000000004,Ratchaburi,Thailand,0,1,0,0,0,0,0,1
+Novotel Bangkok Suvarnabhumi Airport,Lat Krabang,7.9,8.4,Superior Twin Room,2 twin beds,345.41,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+Novotel Bangkok Suvarnabhumi Airport,Lat Krabang,7.9,8.4,Superior Twin Room,2 twin beds,345.41,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+Pullman Pattaya Hotel G,North Pattaya,7.8,8.1,Deluxe Twin Room,2 twin beds,195.389,Pattaya,Thailand,2,0,0,0,0,0,0,2
+INBOX Living Rimkhong,Chiang Khan,8.0,8.2,Superior Twin Room,2 twin beds,39.593,Chiang Khan,Thailand,2,0,0,0,0,0,0,2
+INBOX Living Rimkhong,Chiang Khan,8.0,8.2,Superior Twin Room,2 twin beds,39.593,Chiang Khan,Thailand,2,0,0,0,0,0,0,2
+ISTY Hotel -SHA Extra Plus,"Si Phum, Chiang Mai",8.6,8.8,Standard Double Room,1 king bed,73.183,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+ARAYA HOTEL,Uttaradit,7.7,0.0,King Room,1 king bed,64.69500000000001,Uttaradit,Thailand,0,0,0,1,0,0,0,1
+ARAYA HOTEL,Uttaradit,7.7,0.0,King Room,1 king bed,64.69500000000001,Uttaradit,Thailand,0,0,0,1,0,0,0,1
+Glai Gan Place Hotel,Sara Buri,7.5,8.1,Standard Double Room,1 king bed,77.172,Sara Buri,Thailand,0,0,0,1,0,0,0,1
+Glai Gan Place Hotel,Sara Buri,7.5,8.1,Standard Double Room,1 king bed,77.172,Sara Buri,Thailand,0,0,0,1,0,0,0,1
+Centara Hotel & Convention Centre Udon Thani,Udon Thani,8.4,8.9,Superior Double or Twin Room,1 king bed,135.649,Udon Thani,Thailand,0,0,0,1,0,0,0,1
+"Splash Beach Resort, Maikhao Phuket - SHA Extra Plus",Mai Khao Beach,7.4,0.0,Deluxe Twin Room No Balcony,2 twin beds,153.443,Mai Khao Beach,Thailand,2,0,0,0,0,0,0,2
+"Splash Beach Resort, Maikhao Phuket - SHA Extra Plus",Mai Khao Beach,7.4,0.0,Deluxe Twin Room No Balcony,2 twin beds,153.443,Mai Khao Beach,Thailand,2,0,0,0,0,0,0,2
+BN Resort,Ban Nong Chum Saeng,7.8,0.0,Studio with Lake View,1 king bed,42.052,Ban Nong Chum Saeng,Thailand,0,0,0,1,0,0,0,1
+BN Resort,Ban Nong Chum Saeng,7.8,0.0,Studio with Lake View,1 king bed,42.052,Ban Nong Chum Saeng,Thailand,0,0,0,1,0,0,0,1
+Khong Chiam Orchid Riverside Resort,Khong Chiam,8.5,8.7,Quadruple Room with Garden View,2 full beds,271.072,Khong Chiam,Thailand,0,2,0,0,0,0,0,2
+Khong Chiam Orchid Riverside Resort,Khong Chiam,8.5,8.7,Quadruple Room with Garden View,2 full beds,271.072,Khong Chiam,Thailand,0,2,0,0,0,0,0,2
+BlueSotel SMART Krabi Aonang Beach - Adults only - SHA Extra Plus,Ao Nang Beach,8.6,8.9,Double Room with Pool View,   1 double or 2 twins,166.975,Krabi,Thailand,2,0,0,0,0,0,0,2
+Baan Sabaijai Resort & Rehab,That Phanom,8.1,8.4,Small Double Room,1 queen bed,45.286,That Phanom,Thailand,0,0,1,0,0,0,0,1
+Baan Sabaijai Resort & Rehab,That Phanom,8.1,8.4,Small Double Room,1 queen bed,45.286,That Phanom,Thailand,0,0,1,0,0,0,0,1
+The Gems Mining Pool Villas Pattaya - SHA Extra Plus,Nong Prue,9.0,9.3,Topaz Jacuzzi,2 twin beds,400.493,Nong Prue,Thailand,2,0,0,0,0,0,0,2
+The Gems Mining Pool Villas Pattaya - SHA Extra Plus,Nong Prue,9.0,9.3,Topaz Jacuzzi,2 twin beds,400.493,Nong Prue,Thailand,2,0,0,0,0,0,0,2
+Kalima Resort and Villas Khao Lak - SHA EXTRA PLUS,Khao Lak,8.8,9.1,Deluxe Double or Twin Room,Multiple bed types,267.172,Phang Nga,Thailand,0,0,0,0,0,0,0,0
+Rnana Grand,Phangnga,8.1,9.0,Standard Double Room,1 king bed,67.667,Phangnga,Thailand,0,0,0,1,0,0,0,1
+Rnana Grand,Phangnga,8.1,9.0,Standard Double Room,1 king bed,67.667,Phangnga,Thailand,0,0,0,1,0,0,0,1
+Taakradan Valley Resort  ,Si Sawat,8.0,8.3,Superior Twin Room,2 queen beds,56.069,Si Sawat,Thailand,0,0,2,0,0,0,0,2
+Taakradan Valley Resort  ,Si Sawat,8.0,8.3,Superior Twin Room,2 queen beds,56.069,Si Sawat,Thailand,0,0,2,0,0,0,0,2
+Jomkitti Boutique Hotel,"Si Phum, Chiang Mai",8.9,9.4,Deluxe King with Balcony,1 king bed,178.274,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Fortune D Hotel Loei,Loei,7.7,0.0,Superior Double Room,1 full bed,67.293,Loei,Thailand,0,1,0,0,0,0,0,1
+Fortune D Hotel Loei,Loei,7.7,0.0,Superior Double Room,1 full bed,67.293,Loei,Thailand,0,1,0,0,0,0,0,1
+Pongsin Resort,Khun Han,9.2,9.4,Deluxe Double Room,1 king bed,54.991,Khun Han,Thailand,0,0,0,1,0,0,0,1
+Pongsin Resort,Khun Han,9.2,9.4,Deluxe Double Room,1 king bed,54.991,Khun Han,Thailand,0,0,0,1,0,0,0,1
+Hotel de l'amour SHA PLUS,Prakhon Chai,8.6,9.1,Premier Suite,2 king beds,184.381,Prakhon Chai,Thailand,0,0,0,2,0,0,0,2
+Hotel de l'amour SHA PLUS,Prakhon Chai,8.6,9.1,Premier Suite,2 king beds,184.381,Prakhon Chai,Thailand,0,0,0,2,0,0,0,2
+The Harmony Ville,Phitsanulok,8.2,8.5,Deluxe Double Room with Bath,1 queen bed,67.949,Phitsanulok,Thailand,0,0,1,0,0,0,0,1
+T2 Adira Pattaya,Jomtien Beach,9.1,9.2,Premier King Room,1 full bed,55.841,Pattaya,Thailand,0,1,0,0,0,0,0,1
+Centara Korat,Nakhon Ratchasima,8.9,9.3,Deluxe King,1 king bed,125.46000000000001,Nakhon Ratchasima,Thailand,0,0,0,1,0,0,0,1
+Centara Korat,Nakhon Ratchasima,8.9,9.3,Deluxe King,1 king bed,125.46000000000001,Nakhon Ratchasima,Thailand,0,0,0,1,0,0,0,1
+Koh Ma Beach Resort - SHA Extra Plus,Mae Haad,7.7,8.1,Deluxe Double Room,1 full bed,118.694,Mae Haad,Thailand,0,1,0,0,0,0,0,1
+Koh Ma Beach Resort - SHA Extra Plus,Mae Haad,7.7,8.1,Deluxe Double Room,1 full bed,118.694,Mae Haad,Thailand,0,1,0,0,0,0,0,1
+The Ring Border,Ban Khlong Phruan,8.3,8.7,Superior Double Room,1 king bed,61.537,Ban Khlong Phruan,Thailand,0,0,0,1,0,0,0,1
+The Ring Border,Ban Khlong Phruan,8.3,8.7,Superior Double Room,1 king bed,61.537,Ban Khlong Phruan,Thailand,0,0,0,1,0,0,0,1
+ON Thapae Chiangmai,"Chang Moi, Chiang Mai",9.4,9.7,Standard Double Room,1 king bed,158.50300000000001,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Feungfah Litz,Phetchabun,8.3,8.9,Standard King Room,1 king bed,44.75,Phetchabun,Thailand,0,0,0,1,0,0,0,1
+Feungfah Litz,Phetchabun,8.3,8.9,Standard King Room,1 king bed,44.75,Phetchabun,Thailand,0,0,0,1,0,0,0,1
+"Holiday Inn Express Phuket Patong Beach Central, an IHG Hotel - SHA Plus",Patong Beach,8.0,8.5,Standard Room,   1 double or 2 twins,175.43200000000002,Phuket,Thailand,2,0,0,0,0,0,0,2
+Chatrium Golf Resort Soi Dao Chanthaburi,Ban Thap Sai,8.7,8.8,Deluxe Mountain View Twin Bed,2 twin beds,192.976,Ban Thap Sai,Thailand,2,0,0,0,0,0,0,2
+Chatrium Golf Resort Soi Dao Chanthaburi,Ban Thap Sai,8.7,8.8,Deluxe Mountain View Twin Bed,2 twin beds,192.976,Ban Thap Sai,Thailand,2,0,0,0,0,0,0,2
+Baan Nern Khao Resort Pattaya,Ban Huai Yai,9.3,9.4,Two-Bedroom House,2 queen beds,264.926,Ban Huai Yai,Thailand,0,0,2,0,0,0,0,2
+Baan Nern Khao Resort Pattaya,Ban Huai Yai,9.3,9.4,Two-Bedroom House,2 queen beds,264.926,Ban Huai Yai,Thailand,0,0,2,0,0,0,0,2
+Novotel Bangkok Future Park Rangsit,Pathum Thani,8.7,9.4,Superior Twin Room,2 twin beds,136.364,Pathum Thani,Thailand,2,0,0,0,0,0,0,2
+Novotel Bangkok Future Park Rangsit,Pathum Thani,8.7,9.4,Superior Twin Room,2 twin beds,136.364,Pathum Thani,Thailand,2,0,0,0,0,0,0,2
+  (StockHome Khao Yai),Khanong Phra,8.9,9.1,Standard Double or Twin Room,1 king bed,135.749,Khanong Phra,Thailand,0,0,0,1,0,0,0,1
+  (StockHome Khao Yai),Khanong Phra,8.9,9.1,Standard Double or Twin Room,1 king bed,135.749,Khanong Phra,Thailand,0,0,0,1,0,0,0,1
+RED PEARL BEACH RESORT,Chaloklum,8.3,8.5,King Room with Pool View,1 king bed,136.895,Ko Phangan,Thailand,0,0,0,1,0,0,0,1
+Thong Nai Pan Beach Residence,Thong Nai Pan Yai,8.9,8.8,Deluxe Double Room with Balcony,1 king bed,194.085,Not specified,Thailand,0,0,0,1,0,0,0,1
+Mamaungpaa Hill resort,Ta Khli,8.0,8.0,Triple Room with Pool View,"2 beds (1 twin, 1 queen)",74.399,Ta Khli,Thailand,1,0,1,0,0,0,0,2
+Mela Garden Retreat Cottage,Muak Lek District,8.1,8.1,Double Room with Terrace,Multiple bed types,175.288,Muak Lek District,Thailand,0,0,0,0,0,0,0,0
+Mookies Bungalows,Ko Mook,9.0,8.9,Bungalow,1 full bed,67.098,Ko Mook,Thailand,0,1,0,0,0,0,0,1
+The Garden 304,"304 Industrial Park, Si Maha Phot",8.5,9.1,1-Bedroom Deluxe City View,1 twin bed,98.158,Prachin Buri,Thailand,1,0,0,0,0,0,0,1
+Nautilus Right on the Beach - Adult Only - SHA Extra Plus,"Phra Ae Beach, Ko Lanta",9.0,9.1,Standard Double Room,1 king bed,154.88,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Emporium Suites by Chatrium,"Khlong Toei, Bangkok",8.4,8.7,Deluxe King Room,1 king bed,320.24,Bangkok,Thailand,0,0,0,1,0,0,0,1
+"Sarikantang Resort & Spa, Koh Phangan","Leela Beach, Haad Rin",8.3,8.6,Deluxe Double or Twin Room with Pool View,1 queen bed,224.80100000000002,Haad Rin,Thailand,0,0,1,0,0,0,0,1
+Siam View Hotel and Residence,Pattaya,8.6,8.7,Standard Double Room,1 queen bed,95.879,Pattaya,Thailand,0,0,1,0,0,0,0,1
+Ray Hotel Buriram,Buriram,8.1,8.1,Deluxe Double Room,1 king bed,76.571,Buriram,Thailand,0,0,0,1,0,0,0,1
+Wind Beach Resort,"Sairee Beach, Ko Tao",8.0,8.2,King Room with Balcony,1 full bed,120.98,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Centara Grand Mirage Beach Resort Pattaya - SHA Extra Plus,North Pattaya,8.4,8.5,Deluxe Ocean View King,1 king bed,401.398,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Hilton Hua Hin Resort & Spa,"Hua Hin Night Market, Hua Hin",8.2,8.7,Classic Twin Room with Ocean View,2 twin beds,287.322,Hua Hin,Thailand,2,0,0,0,0,0,0,2
+Citygate Kamala Resort and Residence,Kamala Beach,7.4,0.0,Junior Suite King,"2 beds (1 king, 1 sofa bed)",98.634,Kamala Beach,Thailand,0,0,0,1,0,0,0,2
+Citygate Kamala Resort and Residence,Kamala Beach,7.4,0.0,Junior Suite King,"2 beds (1 king, 1 sofa bed)",98.634,Kamala Beach,Thailand,0,0,0,1,0,0,0,2
+Sun Moon Hotel,Loei,7.2,0.0,Standard Double Room,1 queen bed,38.17,Loei,Thailand,0,0,1,0,0,0,0,1
+Sun Moon Hotel,Loei,7.2,0.0,Standard Double Room,1 queen bed,38.17,Loei,Thailand,0,0,1,0,0,0,0,1
+Chill House ,Ban Mae Salap,8.3,8.5,Superior Queen Room,1 twin bed,36.044000000000004,Ban Mae Salap,Thailand,1,0,0,0,0,0,0,1
+JK LIVING Hotel And Service Apartment,Chachoengsao,8.9,9.2,Standard King Size Bed,1 king bed,65.25,Chachoengsao,Thailand,0,0,0,1,0,0,0,1
+Legendha Sukhothai Hotel - SHA certified,"Mueang Kao, Sukhothai",8.5,8.8,Superior Double or Twin Room,Multiple bed types,146.821,Sukhothai,Thailand,0,0,0,0,0,0,0,0
+Tour De Phuket Hotel - SHA Plus,Thalang,8.4,9.0,Deluxe Double or Twin Room,Multiple bed types,57.387,Thalang,Thailand,0,0,0,0,0,0,0,0
+Phu Morinn Cafe&Camping,Mon Jam,8.1,8.0,Budget Double Room,1 twin bed,186.322,Mon Jam,Thailand,1,0,0,0,0,0,0,1
+"Sindhorn Midtown Hotel Bangkok, Vignette Collection - an IHG Hotel","Pathumwan, Bangkok",9.0,9.5,Premium Twin Room,2 twin beds,330.051,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Chao Phraya Home,Ban Bon,7.7,0.0,Double Room,1 queen bed,35.744,Ban Bon,Thailand,0,0,1,0,0,0,0,1
+Tanouy Garden,Baan Khai,8.1,0.0,One-Bedroom House,2 twin beds,123.105,Baan Khai,Thailand,2,0,0,0,0,0,0,2
+Khao Sok River & Jungle Resort,Khao Sok National Park,9.1,9.0,Queen Room with Garden View,1 queen bed,86.756,Surat Thani,Thailand,0,0,1,0,0,0,0,1
+Suansin Lanna Hotel,Tak,7.8,8.1,Superior King Room,1 queen bed,38.817,Tak,Thailand,0,0,1,0,0,0,0,1
+Ratana Hotel Rassada - SHA Extra Plus,Phuket,7.8,8.4,Superior Double or Twin Room,Multiple bed types,70.98,Phuket,Thailand,0,0,0,0,0,0,0,0
+De Chai the Deco Chiang Mai - SHA Plus,"Chang Moi, Chiang Mai",8.9,9.4,Two bedroom Premier Suite,2 king beds,331.228,Chiang Mai,Thailand,0,0,0,2,0,0,0,2
+Koh Tao Heritage,Ko Tao,8.3,8.7,Superior Triple Room with Sea View,"2 beds (1 twin, 1 king)",163.221,Ko Tao,Thailand,1,0,0,1,0,0,0,2
+BEAR’S DEN HOTEL PATTAYA,Jomtien Beach,7.9,8.3,Twin Room with Balcony,2 twin beds,68.845,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Pru Valley Thaley Tai Resort,Ban Don Phlap (1),8.6,9.0,King Room with Garden View,1 king bed,61.46,Ban Don Phlap,Thailand,0,0,0,1,0,0,0,1
+PhiPhi Andaman Resort,"Tonsai Bay, Phi Phi Islands",7.3,0.0,Standard Queen Room,1 full bed,101.479,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+Avani Plus Khao Lak Resort,"Bangsak Beach, Khao Lak",9.2,9.5,Deluxe Room,1 king bed,231.915,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+Avani Plus Khao Lak Resort,"Bangsak Beach, Khao Lak",9.2,9.5,Deluxe Room,1 king bed,231.915,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+TUI BLUE Khao Lak Resort - SHA Plus,"Khao Lak Beach, Khao Lak",8.8,9.4,Deluxe Double or Twin Room - Adults Only,   1 double or 2 twins,126.22,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+M Village Hostel,"Dannok, Sadao",8.1,8.4,Deluxe Twin Room,2 twin beds,35.582,Dannok,Thailand,2,0,0,0,0,0,0,2
+"Coombs Cottage, Khaoyai, Mu Si",Ban Rai Khlong Sai,8.7,8.9,Three-Bedroom House,"4 beds (2 twins, 2 queens)",375.305,Ban Rai Khlong Sai,Thailand,2,0,2,0,0,0,0,4
+Zenseana Resort & Spa - SHA Plus,Patong Beach,8.0,8.6,Premier Room,Multiple bed types,152.773,Phuket,Thailand,0,0,0,0,0,0,0,0
+Boutique Resort Private Pool Villa - SHA Extra Plus,Ban Pa Khlok,8.5,8.9,"2 Bedroom Suite Private Pool Villa With Bathtub (Free airport pick up minimum 3 nights, 25% discount on food and beverages)","3 beds (2 twins, 1 king)",327.195,Ban Pa Khlok,Thailand,2,0,0,1,0,0,0,3
+Hideaway Resort Banchang,Ban Chang,9.3,9.6,Deluxe Studio,1 queen bed,126.294,Ban Chang,Thailand,0,0,1,0,0,0,0,1
+Bandara Phuket Beach Resort - SHA Extra Plus,Panwa Beach,8.3,8.7,Superior Double or Twin Room,Multiple bed types,141.226,Phuket,Thailand,0,0,0,0,0,0,0,0
+Ascott Embassy Sathorn Bangkok - SHA Plus Certified,"Sathorn, Bangkok",8.9,9.4,Deluxe King Room,1 queen bed,306.325,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Shama Yen-Akat Bangkok,"Sathorn, Bangkok",9.2,9.5,King Room with Balcony,1 king bed,162.952,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Oriental Residence Bangkok,"Pathumwan, Bangkok",8.8,9.2,Junior Suite King,1 king bed,398.516,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Privacy Residence Lopburi,Lop Buri,8.1,8.3,Standard Double Room,1 queen bed,38.17,Lop Buri,Thailand,0,0,1,0,0,0,0,1
+Poonsiri Resort Aonang-SHA Extra Plus,Ao Nang Beach,8.7,8.9,Superior Twin Room,"3 beds (2 twins, 1 sofa bed)",180.732,Krabi,Thailand,2,0,0,0,0,0,0,3
+The Mud - SHA Plus Certified,Ban Bang Po,8.4,8.9,Deluxe Villa,1 king bed,203.477,Ban Bang Po,Thailand,0,0,0,1,0,0,0,1
+Santhiya Phuket Natai Resort & Spa,Natai Beach,8.8,9.3,Deluxe Pool Suite,"2 beds (1 king, 1 sofa bed)",344.891,Natai Beach,Thailand,0,0,0,1,0,0,0,2
+Melia Koh Samui - SHA Extra Plus,Choeng Mon Beach,9.2,9.5,Deluxe Room,Multiple bed types,464.384,Choeng Mon Beach,Thailand,0,0,0,0,0,0,0,0
+The O-Zone Airport Inn,Ban Khlong Prawet,7.5,0.0,Standard King Room,1 king bed,58.225,Ban Khlong Prawet,Thailand,0,0,0,1,0,0,0,1
+The Space Hotel Lampang,Lampang,8.3,8.9,Deluxe King Room,1 queen bed,83.479,Lampang,Thailand,0,0,1,0,0,0,0,1
+The Space Hotel Lampang,Lampang,8.3,8.9,Deluxe King Room,1 queen bed,83.479,Lampang,Thailand,0,0,1,0,0,0,0,1
+Cave Lodge,Pang Mapha,9.0,8.5,Queen Room with Balcony,1 full bed,57.579,Pang Mapha,Thailand,0,1,0,0,0,0,0,1
+A-Te Chumphon Hotel,Chumphon,7.9,8.2,Deluxe Double Room,Multiple bed types,122.74900000000001,Chumphon,Thailand,0,0,0,0,0,0,0,0
+"S Bangkok Hotel, Navamin",Ban Khi Sua,8.3,8.9,Standard Twin Room,2 twin beds,84.427,Ban Khi Sua,Thailand,2,0,0,0,0,0,0,2
+Sorin hotel,Surin,8.2,8.5,Deluxe Room,1 queen bed,94.325,Surin,Thailand,0,0,1,0,0,0,0,1
+Sorin hotel,Surin,8.2,8.5,Deluxe Room,1 queen bed,94.325,Surin,Thailand,0,0,1,0,0,0,0,1
+NIDHRA  ,Ban Lat Krachaeng,9.5,9.7,Studio Apartment,1 queen bed,58.641,Ban Lat Krachaeng,Thailand,0,0,1,0,0,0,0,1
+Nampiangdin Boutique Hotel,"Hai Ya, Chiang Mai",8.8,9.2,Lanna Deluxe Double Room,1 full bed,157.209,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+The Spirit Resort Hua Hin,Khao Tao,8.5,10.0,Two-Bedroom Pool Villa,2 queen beds,406.817,Khao Tao,Thailand,0,0,2,0,0,0,0,2
+Grand Fortune Hotel Nakhon Si Thammarat,Nakhon Si Thammarat,8.7,9.1,Superior Twin Room,1 twin bed,142.329,Nakhon Si Thammarat,Thailand,1,0,0,0,0,0,0,1
+Saming Chiang Dao Guest House,Chiang Dao,7.8,8.0,Deluxe Double Room,1 twin bed,22.643,Chiang Dao,Thailand,1,0,0,0,0,0,0,1
+Norn Rimklong,Bangkok Noi,9.1,9.5,Superior King Room with City View,1 king bed,71.164,Bangkok Noi,Thailand,0,0,0,1,0,0,0,1
+Koh Mak Ao Kao White Sand Beach,Ko Mak,8.5,8.8,Villa with Garden View,1 king bed,165.943,Ko Mak,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok Bangna,Bangna,8.0,8.5,Standard Queen Room,1 queen bed,101.34,Bangna,Thailand,0,0,1,0,0,0,0,1
+Citadines Grand Central Sri Racha - SHA Extra Plus,Si Racha,8.8,9.2,Studio Executive,1 queen bed,105.408,Si Racha,Thailand,0,0,1,0,0,0,0,1
+Citadines Grand Central Sri Racha - SHA Extra Plus,Si Racha,8.8,9.2,Studio Executive,1 queen bed,105.408,Si Racha,Thailand,0,0,1,0,0,0,0,1
+The Beach Resort & Residence - SHA Plus,Pathiu,8.0,8.6,Studio,Multiple bed types,142.02100000000002,Pathiu,Thailand,0,0,0,0,0,0,0,0
+Baan Dara Resort,Sara Buri,7.5,0.0,Superior Bungalow,"2 beds (1 twin, 1 queen)",51.109,Sara Buri,Thailand,1,0,1,0,0,0,0,2
+Baan Dara Resort,Sara Buri,7.5,0.0,Superior Bungalow,"2 beds (1 twin, 1 queen)",51.109,Sara Buri,Thailand,1,0,1,0,0,0,0,2
+Forgotten Hostel Silom,"Silom, Bang Rak",8.5,8.9,Standard Twin Room,2 twin beds,82.46000000000001,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Pullman Phuket Arcadia Karon Beach Resort,Karon Beach,8.4,8.9,Superior Twin Room with Garden View,2 twin beds,274.954,Phuket,Thailand,2,0,0,0,0,0,0,2
+The Proud Exclusive Hotel-SHA Plus,Nakhon Pathom,7.7,8.2,Superior,1 king bed,63.806000000000004,Nakhon Pathom,Thailand,0,0,0,1,0,0,0,1
+Maewin Guest House and Resort,Ban Huai Rin,9.3,8.9,Standard Double Room,1 full bed,90.57300000000001,Ban Huai Rin,Thailand,0,1,0,0,0,0,0,1
+Keeree Loft Resort,Thong Pha Phum,7.2,0.0,Standard Double or Twin Room,Multiple bed types,98.401,Thong Pha Phum,Thailand,0,0,0,0,0,0,0,0
+Maehaad Bay Resort - SHA Plus,Mae Haad,7.9,8.5,Standard Double or Twin Room,   1 double or 2 twins,160.50300000000001,Mae Haad,Thailand,2,0,0,0,0,0,0,2
+Maehaad Bay Resort - SHA Plus,Mae Haad,7.9,8.5,Standard Double or Twin Room,   1 double or 2 twins,160.50300000000001,Mae Haad,Thailand,2,0,0,0,0,0,0,2
+SK Resort Pattaya,Bang Lamung,8.0,8.3,Standard Queen Room,1 king bed,41.34,Bang Lamung,Thailand,0,0,0,1,0,0,0,1
+Nuch's Apple Guest House,Si Bun Ruang,9.2,9.5,Double Room with Garden View,1 queen bed,40.758,Si Bun Ruang,Thailand,0,0,1,0,0,0,0,1
+Coriacea Beachfront Boutique Phuket Resort - SHA Plus,Mai Khao Beach,8.1,8.4,Deluxe Twin Room,2 twin beds,121.44500000000001,Mai Khao Beach,Thailand,2,0,0,0,0,0,0,2
+Coriacea Beachfront Boutique Phuket Resort - SHA Plus,Mai Khao Beach,8.1,8.4,Deluxe Twin Room,2 twin beds,121.44500000000001,Mai Khao Beach,Thailand,2,0,0,0,0,0,0,2
+Green Ville Laguna Hotel,Sung Noen,8.9,9.2,Deluxe Twin Room,2 twin beds,139.159,Sung Noen,Thailand,2,0,0,0,0,0,0,2
+Slive Hotel,Surin,8.8,9.4,Deluxe Twin Room,2 twin beds,102.86500000000001,Surin,Thailand,2,0,0,0,0,0,0,2
+Slive Hotel,Surin,8.8,9.4,Deluxe Twin Room,2 twin beds,102.86500000000001,Surin,Thailand,2,0,0,0,0,0,0,2
+The Wisdom Residence,Bung Kan,7.8,8.1,King Room with Balcony,1 king bed,42.052,Bung Kan,Thailand,0,0,0,1,0,0,0,1
+Summit Windmill Golf Residence,Bangna,8.3,8.8,Presidential Duplex Suite,"4 beds (2 twins, 2 queens)",1585.91,Bangna,Thailand,2,0,2,0,0,0,0,4
+Coco Pina,Prachuap Khiri Khan,9.4,9.5,Two-Bedroom Suite,"3 beds (2 twins, 1 queen)",196.436,Prachuap Khiri Khan,Thailand,2,0,1,0,0,0,0,3
+Cross Vibe Chiang Mai Decem Nimman Hotel - formerly X2 Vibe Chiang Mai Decem - SHA Extra Plus,"Chang Phueak, Chiang Mai",8.4,9.0,Deluxe Room,   1 double or 2 twins,79.518,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Sugar Marina Resort -AVIATOR- Phuket Airport - SHA Extra Plus,Nai Yang Beach,7.7,8.1,Deluxe Room (Exclude Airport Transfer),Multiple bed types,97.20100000000001,Nai Yang Beach,Thailand,0,0,0,0,0,0,0,0
+Sunsuri Phuket - SHA Plus,Nai Harn Beach,8.5,8.8,Superior King or Twin Room,Multiple bed types,245.964,Nai Harn Beach,Thailand,0,0,0,0,0,0,0,0
+,Yasothon,7.8,8.0,Standard King Room,1 king bed,37.847,Yasothon,Thailand,0,0,0,1,0,0,0,1
+Mae Sot Commune,Mae Sot,9.9,9.8,Tent,2 twin beds,2.588,Mae Sot,Thailand,2,0,0,0,0,0,0,2
+Mae Sot Commune,Mae Sot,9.9,9.8,Tent,2 twin beds,2.588,Mae Sot,Thailand,2,0,0,0,0,0,0,2
+Thepchamrat Boutique Hotel,Uttaradit,8.1,8.4,Queen Room with Balcony,1 king bed,37.847,Uttaradit,Thailand,0,0,0,1,0,0,0,1
+Thepchamrat Boutique Hotel,Uttaradit,8.1,8.4,Queen Room with Balcony,1 king bed,37.847,Uttaradit,Thailand,0,0,0,1,0,0,0,1
+Rancho Charnvee Resort & Country Club Khaoyai,Pak Chong,8.4,8.7,One Bedroom Villa,1 queen bed,435.139,Pak Chong,Thailand,0,0,1,0,0,0,0,1
+Phu Fahsai Homestay,Mon Jam,8.1,8.5,Tent,1 queen bed,97.042,Mon Jam,Thailand,0,0,1,0,0,0,0,1
+Chivapuri Beach Resort,"Klong Kloi Beach, Ko Chang",8.4,8.9,Superior Double Room,1 king bed,241.22,Ko Chang,Thailand,0,0,0,1,0,0,0,1
+Coco Bella Hotel,"Loh Dalum Bay, Phi Phi Islands",7.4,0.0,Superior Double Room with Pool View,1 full bed,165.758,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+"Holiday Inn Express Bangkok Siam, an IHG Hotel - SHA Extra Plus","Pathumwan, Bangkok",7.8,8.3,Standard Double or Twin Room,1 full bed,185.08700000000002,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Mida Resort Kanchanaburi - SHA PLUS,Sai Yok,8.0,8.2,Tent River View,2 futon beds,110.462,Sai Yok,Thailand,0,0,0,0,0,0,2,2
+P.L.P Guesthouse - Mae Hong Son,Mae Hong Son,7.8,8.0,Standard Twin Room with Fan,2 twin beds,20.689,Mae Hong Son,Thailand,2,0,0,0,0,0,0,2
+Star 3 Residence,Rayong,8.4,8.7,Standard Double A,1 full bed,39.464,Rayong,Thailand,0,1,0,0,0,0,0,1
+"Holiday Inn Resort Phuket, an IHG Hotel - SHA Extra Plus",Patong Beach,8.9,9.3,1 King Premium Villa Pool View Adults Only,1 king bed,539.928,Phuket,Thailand,0,0,0,1,0,0,0,1
+Mövenpick Myth Hotel Patong Phuket,Patong Beach,8.8,9.1,Classic King Room with Pool View,1 king bed,238.447,Phuket,Thailand,0,0,0,1,0,0,0,1
+Mek Kiri Riverkwai Resort SHA,Thong Pha Phum,7.0,0.0,Superior Twin Room with Garden View,2 twin beds,89.293,Thong Pha Phum,Thailand,2,0,0,0,0,0,0,2
+Ibis Hua Hin,"Khao Takiab, Hua Hin",7.8,8.1,Standard Double Room,1 queen bed,90.262,Hua Hin,Thailand,0,0,1,0,0,0,0,1
+Thai Smile serviced Appartments,Nong Prue,8.0,0.0,Deluxe Double or Twin Room with Balcony,"3 beds (2 twins, 1 full)",24.473,Nong Prue,Thailand,2,1,0,0,0,0,0,3
+Thai Smile serviced Appartments,Nong Prue,8.0,0.0,Deluxe Double or Twin Room with Balcony,"3 beds (2 twins, 1 full)",24.473,Nong Prue,Thailand,2,1,0,0,0,0,0,3
+Oakwood Hotel & Residence Bangkok SHA Plus Certified,"Sathorn, Bangkok",8.1,8.5,Superior Double or Twin Room,   1 double or 2 twins,169.304,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Sugar Marina Resort - NAUTICAL - Kata Beach - SHA plus,"Kata Noi Beach, Kata Beach",8.3,8.5,Deluxe Double or Twin Room,Multiple bed types,151.622,Kata Beach,Thailand,0,0,0,0,0,0,0,0
+Fortune Hotel Korat- SHA Plus,Nakhon Ratchasima,8.0,8.2,Superior Double or Twin Room with City View,   1 double or 2 twins,79.113,Nakhon Ratchasima,Thailand,2,0,0,0,0,0,0,2
+Fortune Hotel Korat- SHA Plus,Nakhon Ratchasima,8.0,8.2,Superior Double or Twin Room with City View,   1 double or 2 twins,79.113,Nakhon Ratchasima,Thailand,2,0,0,0,0,0,0,2
+Hop Inn Mukdahan,Mukdahan,8.2,8.5,Standard Double Room,1 full bed,36.876,Mukdahan,Thailand,0,1,0,0,0,0,0,1
+Hop Inn Mukdahan,Mukdahan,8.2,8.5,Standard Double Room,1 full bed,36.876,Mukdahan,Thailand,0,1,0,0,0,0,0,1
+New Hut Bungalow,Lamai,8.3,8.1,Bungalow with Garden View and  Fan and Shared Bathroom,2 twin beds,34.288000000000004,Lamai,Thailand,2,0,0,0,0,0,0,2
+New Hut Bungalow,Lamai,8.3,8.1,Bungalow with Garden View and  Fan and Shared Bathroom,2 twin beds,34.288000000000004,Lamai,Thailand,2,0,0,0,0,0,0,2
+Lam-tong Resort,Don Sak,8.4,8.2,Deluxe Double Room,1 queen bed,45.286,Don Sak,Thailand,0,0,1,0,0,0,0,1
+Kuiburi Hotel & Resort,Kui Buri,7.1,0.0,Villa with Garden View,1 queen bed,195.79500000000002,Kui Buri,Thailand,0,0,1,0,0,0,0,1
+lyf Sukhumvit 8 Bangkok Managed by The Ascott Limited,"Khlong Toei, Bangkok",8.6,9.1,One of a Kind,1 queen bed,125.44200000000001,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Maikhao Palm Beach Resort - SHA Plus,Mai Khao Beach,7.5,8.2,Deluxe Double or Twin Room with Sea View,1 full bed,269.13100000000003,Mai Khao Beach,Thailand,0,1,0,0,0,0,0,1
+Maikhao Palm Beach Resort - SHA Plus,Mai Khao Beach,7.5,8.2,Deluxe Double or Twin Room with Sea View,1 full bed,269.13100000000003,Mai Khao Beach,Thailand,0,1,0,0,0,0,0,1
+"Valia Hotel Bangkok, Sukhumvit 24","Khlong Toei, Bangkok",8.5,9.1,Deluxe - Tower 2,1 king bed,230.63,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Green Terrace Resort,Chanthaburi,8.3,8.8,Poolside Room,1 full bed,85.913,Chanthaburi,Thailand,0,1,0,0,0,0,0,1
+Papangkorn House,Suratthani,8.1,8.5,Deluxe Double Room,1 full bed,43.669000000000004,Suratthani,Thailand,0,1,0,0,0,0,0,1
+Sheraton Hua Hin Pranburi Villas,"Pak Nam Pran, Pran Buri",8.5,8.7,Villa with Private Pool,1 queen bed,553.554,Pran Buri,Thailand,0,0,1,0,0,0,0,1
+Sunprime Kamala Beach - SHA Plus,Kamala Beach,8.6,9.0,Grand Deluxe Pool View Room,   1 double or 2 twins,252.957,Kamala Beach,Thailand,2,0,0,0,0,0,0,2
+Sunprime Kamala Beach - SHA Plus,Kamala Beach,8.6,9.0,Grand Deluxe Pool View Room,   1 double or 2 twins,252.957,Kamala Beach,Thailand,2,0,0,0,0,0,0,2
+Phi Phi The Beach Resort- SHA Certified,"Long Beach, Phi Phi Islands",8.5,8.7,Superior Villa with Partial Sea View-Free Transfer to Tonsai Pier,1 full bed,340.20300000000003,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+I love phants Lodge,Ban Huai Thawai,8.8,8.6,Twin Room with Garden View,2 twin beds,252.957,Ban Huai Thawai,Thailand,2,0,0,0,0,0,0,2
+The ShellSea Krabi-SHA Extra Plus,Ao Nam Mao,8.6,9.1,Shellsea Garden,   1 double or 2 twins,360.998,Krabi,Thailand,2,0,0,0,0,0,0,2
+The Bed Hotel,Roi Et,8.0,8.4,Standard King Room,1 king bed,42.052,Roi Et,Thailand,0,0,0,1,0,0,0,1
+ibis Styles Bangkok Sukhumvit Phra Khanong,"Khlong Toei, Bangkok",8.2,8.7,Standard Twin Room,2 twin beds,111.566,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Jomtien Hostel,Jomtien Beach,7.9,8.1,Standard Double Room,1 full bed,33.538000000000004,Pattaya,Thailand,0,1,0,0,0,0,0,1
+Baansuan Resort,Don Sak,7.8,8.1,Deluxe Bungalow with Garden View,1 king bed,51.756,Don Sak,Thailand,0,0,0,1,0,0,0,1
+Pullman Khon Kaen Raja Orchid,Khon Kaen,8.2,8.7,Superior Twin Room,"3 beds (2 twins, 1 futon)",125.767,Khon Kaen,Thailand,2,0,0,0,0,0,0,3
+Aziss Boutique Hotel - SHA Plus,Phitsanulok,9.0,9.3,Deluxe King Room,1 king bed,46.249,Phitsanulok,Thailand,0,0,0,1,0,0,0,1
+Ibis Pattaya,Pattaya,7.8,8.2,Standard Twin Room,2 twin beds,68.016,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Garrya Tongsai Bay Samui - SHA Extra Plus,Choeng Mon Beach,9.4,9.5,Beachfront Suites,1 queen bed,717.517,Choeng Mon Beach,Thailand,0,0,1,0,0,0,0,1
+Ibis Bangkok Riverside,"Riverside, Bangkok",8.0,8.3,Twin Room,2 twin beds,102.899,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Sea Seeker Krabi Resort - SHA Plus,Ao Nang Beach,8.7,8.9,Deluxe Double or Twin Room with Sea View,Multiple bed types,180.9,Krabi,Thailand,0,0,0,0,0,0,0,0
+ibis Bangkok Sathorn,"Sathorn, Bangkok",8.2,8.5,Superior Queen Room,1 queen bed,72.699,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Bkk39 Airport hotel,Ban Khlong Prawet,6.5,0.0,Deluxe Double Room with Balcony,"2 beds (1 king, 1 sofa bed)",51.176,Ban Khlong Prawet,Thailand,0,0,0,1,0,0,0,2
+Myplace@Surat Hotel,Suratthani,7.9,8.0,Double Room with Fan,1 full bed,25.813,Suratthani,Thailand,0,1,0,0,0,0,0,1
+Songthai Suvarnabhumi,Ban Khlong Si,8.9,9.0,Twin Room with Balcony,2 twin beds,89.926,Ban Khlong Si,Thailand,2,0,0,0,0,0,0,2
+Somerset Harbourview Sri Racha - SHA Extra Plus,Si Racha,9.2,9.5,Studio Apartment,1 king bed,120.202,Si Racha,Thailand,0,0,0,1,0,0,0,1
+Somerset Harbourview Sri Racha - SHA Extra Plus,Si Racha,9.2,9.5,Studio Apartment,1 king bed,120.202,Si Racha,Thailand,0,0,0,1,0,0,0,1
+Tup Kaek Sunset Beach Resort-SHA Plus,Tab Kaek Beach,8.3,8.7,Deluxe Garden View,1 full bed,272.336,Tab Kaek Beach,Thailand,0,1,0,0,0,0,0,1
+Lee Hotel,Suratthani,8.3,8.6,One-Bedroom Suite,1 king bed,91.867,Suratthani,Thailand,0,0,0,1,0,0,0,1
+Seaview Resort Khao Lak - SHA Plus,"Nang Thong Beach, Khao Lak",8.3,8.7,Deluxe Double or Twin Room with Garden View,Multiple bed types,159.427,Khao Lak,Thailand,0,0,0,0,0,0,0,0
+Grand Mercure Phuket Patong,Patong Beach,8.5,8.9,Superior Twin Room,2 twin beds,261.556,Phuket,Thailand,2,0,0,0,0,0,0,2
+Blu Monkey Hub & Hotel Suratthani - SHA PLUS Certificate,Suratthani,8.4,8.8,Standard King Room,1 king bed,75.75,Suratthani,Thailand,0,0,0,1,0,0,0,1
+Prana Resort Samui,Bangrak Beach,9.2,9.5,Cozy Deluxe Room,Multiple bed types,273.174,Bangrak Beach,Thailand,0,0,0,0,0,0,0,0
+Guesthouse Phuket Airport,Ban Bo Han,7.2,0.0,Deluxe Double Room (2 Adults + 1 Child),1 king bed,55.389,Ban Bo Han,Thailand,0,0,0,1,0,0,0,1
+"The Standard, Hua Hin",Hua Hin,8.7,9.3,Standard King Room,1 king bed,451.26,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+Thanyachatra Boutique,Phetchaburi,7.9,8.1,Deluxe Double Room with Balcony,1 queen bed,42.052,Phetchaburi,Thailand,0,0,1,0,0,0,0,1
+Unity Hotel,Si Sa Ket,8.4,8.8,Double Room,1 full bed,51.756,Si Sa Ket,Thailand,0,1,0,0,0,0,0,1
+VIC 3 Bangkok - SHA Plus,"Phaya Thai, Bangkok",8.0,8.2,Flash Sales Studio Room Only,   1 double or 2 twins,109.877,Bangkok,Thailand,2,0,0,0,0,0,0,2
+"Holiday Inn Express Bangkok Soi Soonvijai, an IHG Hotel","Huai Khwang, Bangkok",7.7,8.1,Standard Double Room,1 queen bed,130.717,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Sea Valley Resort,Lipa Noi,8.6,8.8,Villa with Private Pool and Sea View,1 queen bed,381.7,Ko Samui,Thailand,0,0,1,0,0,0,0,1
+Pai Village Boutique Resort,Pai,9.0,9.2,Boutique Village,1 full bed,167.406,Mae Hong Son,Thailand,0,1,0,0,0,0,0,1
+Kim Jek Cin 2 Hotel - 2,Mukdahan,7.6,0.0,Standard Double Room,1 king bed,23.843,Mukdahan,Thailand,0,0,0,1,0,0,0,1
+Kim Jek Cin 2 Hotel - 2,Mukdahan,7.6,0.0,Standard Double Room,1 king bed,23.843,Mukdahan,Thailand,0,0,0,1,0,0,0,1
+Railay Bay Resort & Spa-SHA Extra Plus,Railay Beach,7.9,8.2,One Bedroom Luxury Pool Villa,"2 beds (1 sofa bed, 1 queen)",1122.92,Railay Beach,Thailand,0,0,1,0,0,0,0,2
+SUNRAY Guesthouse ,Uthai Thani,7.1,0.0,Deluxe Double Room,1 full bed,67.606,Uthai Thani,Thailand,0,1,0,0,0,0,0,1
+The Joy Beach Villas,Hinkong,9.2,9.4,One-Bedroom Villa,"2 beds (1 full, 1 sofa bed)",404.344,Hinkong,Thailand,0,1,0,0,0,0,0,2
+SAii Phi Phi Island Village,"Loh Bagao Bay, Phi Phi Islands",8.5,8.9,Deluxe Premium Bungalow 1 King,1 king bed,543.711,Phi Phi Islands,Thailand,0,0,0,1,0,0,0,1
+JP Emerald Hotel,Yasothon,5.6,0.0,Superior Twin Room,2 twin beds,53.296,Yasothon,Thailand,2,0,0,0,0,0,0,2
+Pugdee Hotel,Lang Suan,7.6,0.0,Standard Double Room,1 full bed,45.286,Lang Suan,Thailand,0,1,0,0,0,0,0,1
+Dusit Thani Pattaya - SHA Extra Plus,North Pattaya,8.3,8.6,One Bedroom Suite Twin,2 twin beds,435.265,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Ramada Plaza by Wyndham Chao Fah Phuket,Phuket,8.1,8.9,Deluxe Twin Room,2 twin beds,125.884,Phuket,Thailand,2,0,0,0,0,0,0,2
+Avani Plus Mai Khao Phuket Suites,Mai Khao Beach,9.0,9.4,One Bedroom Suite,"2 beds (1 king, 1 sofa bed)",515.793,Mai Khao Beach,Thailand,0,0,0,1,0,0,0,2
+Avani Plus Mai Khao Phuket Suites,Mai Khao Beach,9.0,9.4,One Bedroom Suite,"2 beds (1 king, 1 sofa bed)",515.793,Mai Khao Beach,Thailand,0,0,0,1,0,0,0,2
+Sand Sea Resort Railay Beach,Railay Beach,7.4,0.0,Tropical Villa Double Bed (Sunrise Wing),"2 beds (1 king, 1 sofa bed)",232.90200000000002,Railay Beach,Thailand,0,0,0,1,0,0,0,2
+Chiangkham Grand Villa - SHA Certified,Chiang Kham,7.9,8.2,Superior Double or Twin Room,Multiple bed types,64.048,Chiang Kham,Thailand,0,0,0,0,0,0,0,0
+Rajapruek Samui Resort - SHA Plus,Lipa Noi,7.1,0.0,Deluxe Double Room,1 king bed,108.882,Ko Samui,Thailand,0,0,0,1,0,0,0,1
+"Hotel Indigo Bangkok Wireless Road, an IHG Hotel","Pathumwan, Bangkok",8.9,9.4,Standard Room,   1 double or 2 twins,308.628,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Siam Boutique Hotel,Buriram,7.3,0.0,Standard Double Room,1 full bed,34.935,Buriram,Thailand,0,1,0,0,0,0,0,1
+Pinky Bungalow Resort - SHA Extra Plus,"Klong Khong Beach, Ko Lanta",8.7,9.0,Luxury Room,1 king bed,63.632,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Crystal Resort Korat,Nakhon Ratchasima,8.1,8.4,Presidential Suite,"3 beds (1 king, 2 sofa beds)",49.492000000000004,Nakhon Ratchasima,Thailand,0,0,0,1,2,0,0,3
+Crystal Resort Korat,Nakhon Ratchasima,8.1,8.4,Presidential Suite,"3 beds (1 king, 2 sofa beds)",49.492000000000004,Nakhon Ratchasima,Thailand,0,0,0,1,2,0,0,3
+"Nakara Hotel, Ubon Ratchathani",Ban Na Kham,8.7,9.0,Deluxe Double Room,1 queen bed,60.351,Ban Na Kham,Thailand,0,0,1,0,0,0,0,1
+The Sriracha Residence,Si Racha,8.2,8.4,   Triple Room,"2 beds (1 twin, 1 king)",79.852,Si Racha,Thailand,1,0,0,1,0,0,0,2
+The Sriracha Residence,Si Racha,8.2,8.4,   Triple Room,"2 beds (1 twin, 1 king)",79.852,Si Racha,Thailand,1,0,0,1,0,0,0,2
+Lanta Sand Resort & Spa,"Long Beach, Ko Lanta",8.4,8.7,Beach Front Pool Access,1 queen bed,253.788,Ko Lanta,Thailand,0,0,1,0,0,0,0,1
+"Divalux Resort & Spa Bangkok, Suvarnabhumi Airport",Lat Krabang,7.4,8.0,Deluxe Twin Room,2 twin beds,103.502,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+"Divalux Resort & Spa Bangkok, Suvarnabhumi Airport",Lat Krabang,7.4,8.0,Deluxe Twin Room,2 twin beds,103.502,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+Coco Palm Beach Resort - SHA Extra Plus,Mae Nam,8.0,8.0,Superior Bungalow,   1 double or 2 twins,142.329,Ko Samui,Thailand,2,0,0,0,0,0,0,2
+"Phulay Bay, A Ritz-Carlton Reserve",Tab Kaek Beach,9.7,9.9,Reserve Pool Villa,2 king beds,2427.065,Tab Kaek Beach,Thailand,0,0,0,2,0,0,0,2
+"Chanalai Flora Resort, Kata Beach - SHA Plus",Kata Beach,8.5,8.7,Superior Double or Twin Room,Multiple bed types,180.038,Kata Beach,Thailand,0,0,0,0,0,0,0,0
+"Chanalai Flora Resort, Kata Beach - SHA Plus",Kata Beach,8.5,8.7,Superior Double or Twin Room,Multiple bed types,180.038,Kata Beach,Thailand,0,0,0,0,0,0,0,0
+Cape Dara Resort - SHA Plus,North Pattaya,8.6,8.9,Top Star Duplex Suite with Club Benefits,2 king beds,1909.796,Pattaya,Thailand,0,0,0,2,0,0,0,2
+Niwas Ayutthaya,Phra Nakhon Si Ayutthaya,8.1,8.0,Superior Double Room,1 queen bed,73.752,Phra Nakhon Si Ayutthaya,Thailand,0,0,1,0,0,0,0,1
+Sindhorn Kempinski Hotel Bangkok - SHA Extra Plus,"Pathumwan, Bangkok",9.4,9.6,Executive Club Twin Room,2 twin beds,1267.65,Bangkok,Thailand,2,0,0,0,0,0,0,2
+The Living Hotel SamutPrakan,Bang Bo,8.2,8.3,Deluxe Double Room with Balcony,1 king bed,52.209,Bang Bo,Thailand,0,0,0,1,0,0,0,1
+Sunwing Kamala Beach - SHA Plus,Kamala Beach,8.8,9.3,Studio with Pool View,   1 double or 2 twins,296.75600000000003,Kamala Beach,Thailand,2,0,0,0,0,0,0,2
+Sunwing Kamala Beach - SHA Plus,Kamala Beach,8.8,9.3,Studio with Pool View,   1 double or 2 twins,296.75600000000003,Kamala Beach,Thailand,2,0,0,0,0,0,0,2
+Salad Buri Resort- SHA Extra Plus,Salad Beach,7.6,8.0,Premier Double Room,1 queen bed,127.354,Ko Phangan,Thailand,0,0,1,0,0,0,0,1
+The Pride Hotel Phitsanulok,Ban Ko,8.9,9.2,Superior Twin Room,2 twin beds,116.45100000000001,Ban Ko,Thailand,2,0,0,0,0,0,0,2
+Chantaramas Resort,Baan Khai,8.1,8.5,Deluxe Double Room,1 king bed,169.131,Baan Khai,Thailand,0,0,0,1,0,0,0,1
+Salad Beach Resort - SHA Extra Plus,Salad Beach,7.8,8.1,Standard Double Room,1 queen bed,89.556,Ko Phangan,Thailand,0,0,1,0,0,0,0,1
+Hyatt Regency Koh Samui- SHA Extra Plus,Chaweng,9.1,9.4,1 Bedroom Regency Suite,1 king bed,746.202,Chaweng,Thailand,0,0,0,1,0,0,0,1
+Patravana Resort,Phayayen,7.8,8.0,Vana Country View Suite,Multiple bed types,121.233,Nakhon Ratchasima,Thailand,0,0,0,0,0,0,0,0
+dusitD2 Chiang Mai - SHA Extra Plus,"Chang Moi, Chiang Mai",8.5,8.8,Club Deluxe King,1 king bed,228.459,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Four Seasons Resort Chiang Mai -SHA Plus,Mae Rim,9.4,9.6,One Bedroom Residence,"3 beds (2 twins, 1 king)",4804.68,Mae Rim,Thailand,2,0,0,1,0,0,0,3
+Four Seasons Resort Chiang Mai -SHA Plus,Mae Rim,9.4,9.6,One Bedroom Residence,"3 beds (2 twins, 1 king)",4804.68,Mae Rim,Thailand,2,0,0,1,0,0,0,3
+ibis Styles Bangkok Silom,"Bang Rak, Bangkok",8.6,9.1,Standard Twin Room,2 twin beds,125.551,Bangkok,Thailand,2,0,0,0,0,0,0,2
+SKYVIEW Resort Phuket Patong Beach - SHA Extra Plus,Patong Beach,8.9,9.4,Garden Studio,   1 double or 2 twins,238.38,Phuket,Thailand,2,0,0,0,0,0,0,2
+Cholchan Pattaya Beach Resort - SHA Extra Plus,Na Kluea,7.2,0.0,Superior Double or Twin Room with Garden View,   1 double or 2 twins,328.774,Na Kluea,Thailand,2,0,0,0,0,0,0,2
+Cholchan Pattaya Beach Resort - SHA Extra Plus,Na Kluea,7.2,0.0,Superior Double or Twin Room with Garden View,   1 double or 2 twins,328.774,Na Kluea,Thailand,2,0,0,0,0,0,0,2
+ibis Styles Bangkok Ratchada,"Huai Khwang, Bangkok",8.3,8.6,Standard Queen Room,1 full bed,131.916,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Sasitara Thai Villas,Choeng Mon Beach,8.5,8.5,Family Villa,"2 beds (1 twin, 1 full)",206.506,Choeng Mon Beach,Thailand,1,1,0,0,0,0,0,2
+"Bandara Resort and Spa, Samui","Fisherman Village, Bophut",8.4,8.7,Deluxe Double or Twin Room with Balcony,Multiple bed types,265.05,Bophut,Thailand,0,0,0,0,0,0,0,0
+"Bandara Resort and Spa, Samui","Fisherman Village, Bophut",8.4,8.7,Deluxe Double or Twin Room with Balcony,Multiple bed types,265.05,Bophut,Thailand,0,0,0,0,0,0,0,0
+The Royal Bamboo Lodges - SHA Certified,Khao Sok National Park,8.4,8.5,Double Room with Garden View,Multiple bed types,64.048,Surat Thani,Thailand,0,0,0,0,0,0,0,0
+Cassia Phuket - SHA Extra Plus,"Laguna Phuket, Bang Tao Beach",8.3,8.7,One-Bedroom Suite,1 queen bed,318.142,Bang Tao Beach,Thailand,0,0,1,0,0,0,0,1
+7 Days Premium Hotel Don Meaung Airport,Ban Don Muang,8.2,8.6,Superior Twin Room,2 twin beds,76.694,Don Muang,Thailand,2,0,0,0,0,0,0,2
+The Midst@ Royal hills Nakornayok,Nakhon Nayok,8.6,9.0,Two-Bedroom House,"4 beds (2 twins, 1 king, 1 sofa bed)",333.075,Nakhon Nayok,Thailand,2,0,0,1,0,0,0,4
+Dusit Thani Laguna Phuket - SHA Extra Plus,"Laguna Phuket, Bang Tao Beach",8.4,8.6,Deluxe Double or Twin Room with Ocean View,1 king bed,423.038,Bang Tao Beach,Thailand,0,0,0,1,0,0,0,1
+"InterContinental Koh Samui Resort, an IHG Hotel - SHA Extra Plus",Taling Ngam Beach,8.9,9.4,Two-Bedroom Family Villa,"3 beds (2 twins, 1 king)",1317.972,Taling Ngam Beach,Thailand,2,0,0,1,0,0,0,3
+Grand Mercure Khao Lak Bangsak,"Bangsak Beach, Khao Lak",9.0,9.4,Deluxe Twin Room,2 twin beds,148.346,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+Grand Mercure Khao Lak Bangsak,"Bangsak Beach, Khao Lak",9.0,9.4,Deluxe Twin Room,2 twin beds,148.346,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+Bandahara,Satun,8.1,8.1,Apartment with Balcony,1 full bed,52.403,Satun,Thailand,0,1,0,0,0,0,0,1
+"Supalai Scenic Bay Resort And Spa, SHA Extra Plus",Por Bay,7.6,8.4,Super Deluxe Double room with Sea view,1 king bed,121.294,Por Bay,Thailand,0,0,0,1,0,0,0,1
+Sofitel Krabi Phokeethra Golf and Spa Resort,Klong Muang Beach,8.4,9.2,Superior Room with King Bed and Balcony,1 king bed,308.195,Klong Muang Beach,Thailand,0,0,0,1,0,0,0,1
+Sofitel Krabi Phokeethra Golf and Spa Resort,Klong Muang Beach,8.4,9.2,Superior Room with King Bed and Balcony,1 king bed,308.195,Klong Muang Beach,Thailand,0,0,0,1,0,0,0,1
+Serenity Hotel and Spa Kabinburi,Kabin Buri,8.7,8.9,Executive Pool View include Onsen,1 king bed,226.43200000000002,Kabin Buri,Thailand,0,0,0,1,0,0,0,1
+MEET Boutique Resort,Suratthani,8.2,8.6,Deluxe King Room,1 king bed,115.804,Suratthani,Thailand,0,0,0,1,0,0,0,1
+Mövenpick Siam Hotel Na Jomtien Pattaya,Na Jomtien,8.2,8.5,Deluxe Twin Room with Sea View,2 twin beds,304.867,Pattaya,Thailand,2,0,0,0,0,0,0,2
+The Orchid Resort & Relax,Maha Sarakham,7.8,8.0,Deluxe Bungalow with Garden View,1 king bed,54.991,Maha Sarakham,Thailand,0,0,0,1,0,0,0,1
+Rabbit Hotel Phimai,Phimai,7.7,8.1,Superior Double Room,1 king bed,66.83800000000001,Phimai,Thailand,0,0,0,1,0,0,0,1
+Phanomrungpuri Hotel Buriram,Nang Rong,8.5,8.8,Superior King Room,1 king bed,77.569,Nang Rong,Thailand,0,0,0,1,0,0,0,1
+"Holiday Inn Express Pattaya Central, an IHG Hotel - SHA Extra Plus",Pattaya,8.5,9.1,Standard Queen Room - Non-Smoking,1 queen bed,92.025,Pattaya,Thailand,0,0,1,0,0,0,0,1
+Carlton Hotel Bangkok Sukhumvit - SHA Extra Plus,"Wattana, Bangkok",9.3,9.7,Executive Room,Multiple bed types,478.958,Bangkok,Thailand,0,0,0,0,0,0,0,0
+ibis Styles Krabi Ao Nang,Ao Nang Beach,8.3,8.5,Standard Twin Room,2 twin beds,79.048,Krabi,Thailand,2,0,0,0,0,0,0,2
+"Kimpton Kitalay Samui, an IHG Hotel",Choeng Mon Beach,9.3,9.7,Standard Double Room,1 king bed,631.289,Choeng Mon Beach,Thailand,0,0,0,1,0,0,0,1
+Dream riverside,Ban Rai,8.6,9.2,Quadruple Room with Mountain View,4 bunk beds,74.399,Ban Rai,Thailand,0,0,0,0,0,4,0,4
+Noku Phuket,Chalong,9.1,9.5,Loft Studio with Twin Bed,2 twin beds,552.883,Phuket,Thailand,2,0,0,0,0,0,0,2
+De Chai Colonial Hotel & Spa - SHA Plus,"Chang Moi, Chiang Mai",8.7,9.1,Grand Deluxe Double or Twin Room (Room Only),   1 double or 2 twins,171.925,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Dinso Resort & Villas Phuket an IHG Hotel,"Nanai Road, Patong Beach",8.8,9.1,Premium Room,,264.387,Patong Beach,Thailand,0,0,0,0,0,0,0,0
+Supachai Inn,Ban Ba Ngan,7.9,8.2,Premium Quadruple Room,2 king beds,54.344,Not specified,Thailand,0,0,0,2,0,0,0,2
+Hua Hin Marriott Resort and Spa,Hua Hin,8.8,9.3,"Superior, Guest room, 2 Double, Resort view",2 queen beds,391.847,Hua Hin,Thailand,0,0,2,0,0,0,0,2
+The Sea Koh Samui Resort and Residences by Tolani - SHA Extra Plus,Koh Samui,7.9,8.4,Deluxe Suite Sea View,1 king bed,336.71,Koh Samui,Thailand,0,0,0,1,0,0,0,1
+Hotel Thomas Bangkok,Makkasan,8.4,8.8,Suite with Terrace,"2 beds (1 sofa bed, 1 queen)",542.206,Makkasan,Thailand,0,0,1,0,0,0,0,2
+Wyndham Hua Hin Pranburi Resort & Villas,"Pak Nam Pran, Ban Pak Nam Pran",9.0,9.2,Garden Twin Room,2 twin beds,216.035,Pak Nam Pran,Thailand,2,0,0,0,0,0,0,2
+Sugar Marina Resort-SURF-Kata Beach - SHA Plus,Kata Beach,7.9,8.0,Deluxe Double or Twin Room,Multiple bed types,132.851,Kata Beach,Thailand,0,0,0,0,0,0,0,0
+Sugar Marina Resort-SURF-Kata Beach - SHA Plus,Kata Beach,7.9,8.0,Deluxe Double or Twin Room,Multiple bed types,132.851,Kata Beach,Thailand,0,0,0,0,0,0,0,0
+Hop Inn Mae Sot,Mae Sot,8.4,8.6,Standard  Double Room,1 full bed,40.111000000000004,Mae Sot,Thailand,0,1,0,0,0,0,0,1
+Hop Inn Mae Sot,Mae Sot,8.4,8.6,Standard  Double Room,1 full bed,40.111000000000004,Mae Sot,Thailand,0,1,0,0,0,0,0,1
+Pullman Phuket Panwa Beach Resort,Panwa Beach,8.1,8.6,Deluxe King Room,1 king bed,252.89600000000002,Phuket,Thailand,0,0,0,1,0,0,0,1
+Bangrak Pier Bungalow,Bangrak Beach,8.9,9.1,Bungalow with Garden View,1 king bed,118.53,Bangrak Beach,Thailand,0,0,0,1,0,0,0,1
+Baan Tonjaeng Resort,Chai Nat,9.0,9.2,Deluxe Double Room,1 king bed,61.46,Chai Nat,Thailand,0,0,0,1,0,0,0,1
+ibis Bangkok Sukhumvit 24,"Khlong Toei, Bangkok",8.2,8.6,Standard Queen Room,1 queen bed,112.277,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Lada Krabi Residence Hotel - SHA Plus,Krabi,8.2,8.5,Superior Double Room,1 full bed,64.339,Krabi,Thailand,0,1,0,0,0,0,0,1
+Pimalai Resort & Spa - SHA Extra Plus,"Kan Tiang Beach, Ko Lanta",9.6,9.7,Deluxe Room - Free Krabi Airport Transfer,1 queen bed,827.816,Ko Lanta,Thailand,0,0,1,0,0,0,0,1
+Shangrilah Bungalow,Mae Nam,8.5,8.5,Standard Double Room,1 full bed,93.161,Ko Samui,Thailand,0,1,0,0,0,0,0,1
+Hotel Zing,Pattaya South,8.1,8.4,Superior Double Room South Wing,1 full bed,61.647,Pattaya,Thailand,0,1,0,0,0,0,0,1
+The deer resort,Chiang Mai,7.8,8.4,Twin Room with Bath,2 twin beds,101.796,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Cross Pattaya Pratamnak - SHA Plus,Pattaya South,8.9,9.4,Cross - Elite Suite,1 king bed,433.476,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok Sukhumvit 4,"Khlong Toei, Bangkok",8.4,9.0,Deluxe Twin Room,2 twin beds,175.964,Bangkok,Thailand,2,0,0,0,0,0,0,2
+SYLVAN Koh Chang,"Kai Bae Beach, Ko Chang",8.5,8.9,Grand Deluxe Sea View,Multiple bed types,223.69400000000002,Ko Chang,Thailand,0,0,0,0,0,0,0,0
+The Grass Serviced Suites,Pattaya,7.9,8.1,Suite,1 full bed,76.047,Pattaya,Thailand,0,1,0,0,0,0,0,1
+Siam Mandarina Hotel - Suvarnabhumi Airport,Lat Krabang,7.7,8.3,Deluxe Twin Room,2 twin beds,168.207,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+Siam Mandarina Hotel - Suvarnabhumi Airport,Lat Krabang,7.7,8.3,Deluxe Twin Room,2 twin beds,168.207,Lat Krabang,Thailand,2,0,0,0,0,0,0,2
+Lanta Pearl Beach Resort,"Long Beach, Ko Lanta",8.8,9.1,Bungalow with Air Conditioning,1 king bed,73.19800000000001,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Koh Kood Garden View,"Klong Chao Beach, Ko Kood",8.1,0.0,Standard Bungalow with Fan,1 queen bed,52.403,Ko Kood,Thailand,0,0,1,0,0,0,0,1
+We Briza Hotel Chiangmai,"Chang Moi, Chiang Mai",7.4,0.0,Deluxe Triple Room,Multiple bed types,42.618,Chiang Mai,Thailand,0,0,0,0,0,0,0,0
+Mövenpick Hotel Sukhumvit 15 Bangkok,"Wattana, Bangkok",7.7,8.2,Superior King Room,1 king bed,178.681,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Stay Wellbeing & Lifestyle Resort,Rawai Beach,9.0,9.5,Junior Suite with Garden View,Multiple bed types,261.839,Phuket,Thailand,0,0,0,0,0,0,0,0
+Mercure Pattaya Ocean Resort,Pattaya,7.7,8.1,Superior Twin Room,2 twin beds,148.241,Pattaya,Thailand,2,0,0,0,0,0,0,2
+OYO 628 Dao Krajang,Phetchaburi,7.1,0.0,Standard Double Room,1 queen bed,27.886,Phetchaburi,Thailand,0,0,1,0,0,0,0,1
+ibis Bangkok Siam,"Pathumwan, Bangkok",8.1,8.5,Standard Double Room,1 queen bed,144.39000000000001,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Hop Inn Roi Et,Roi Et,8.2,8.3,Standard Twin Room,2 twin beds,39.187,Roi Et,Thailand,2,0,0,0,0,0,0,2
+Thezen Hotel,Yasothon,6.4,8.0,Double Room,1 full bed,51.756,Yasothon,Thailand,0,1,0,0,0,0,0,1
+"Holiday Ao Nang Beach Resort, Krabi - SHA Extra Plus","Nopparat Thara Beach, Ao Nang Beach",7.8,8.1,Family Room with Garden View,   1 double or 2 twins,487.687,Krabi,Thailand,2,0,0,0,0,0,0,2
+"Holiday Inn Bangkok Silom, an IHG Hotel","Bang Rak, Bangkok",8.5,8.9,Deluxe Room with Two Beds - Smoking,2 twin beds,184.309,Bangkok,Thailand,2,0,0,0,0,0,0,2
+NORN Nimman13 Boutique Hotel Chiang Mai,"Nimmanhaemin, Chiang Mai",8.8,9.1,Superior Queen Room,"2 beds (1 twin, 1 queen)",131.936,Chiang Mai,Thailand,1,0,1,0,0,0,0,2
+,Uthai Thani,8.5,8.6,Double Room with Mountain View,1 king bed,45.286,Uthai Thani,Thailand,0,0,0,1,0,0,0,1
+Royal Cliff Beach Hotel - SHA Extra Plus,Pattaya South,8.3,8.7,Mini Suite Plus Sun Rise Twin Bed,Multiple bed types,289.261,Pattaya,Thailand,0,0,0,0,0,0,0,0
+Baan Suan Leelawadee Resort Nan,Nan,9.0,9.1,Standard Double Room,1 king bed,82.971,Nan,Thailand,0,0,0,1,0,0,0,1
+Sai Naam Lanta Residence SHA Plus,"Phra Ae Beach, Ko Lanta",8.1,8.4,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",156.192,Ko Lanta,Thailand,0,0,1,0,0,0,0,2
+Khunyuam Resort,Ban Khun Yuam,8.8,8.9,Budget Twin Room,2 twin beds,41.243,Ban Khun Yuam,Thailand,2,0,0,0,0,0,0,2
+Proud Phu Fah Hip & Green Resort,Mae Rim,8.8,9.0,Tree Top for 2 people,1 queen bed,361.737,Mae Rim,Thailand,0,0,1,0,0,0,0,1
+Proud Phu Fah Hip & Green Resort,Mae Rim,8.8,9.0,Tree Top for 2 people,1 queen bed,361.737,Mae Rim,Thailand,0,0,1,0,0,0,0,1
+Krabi Golden Hill Hotel,Krabi,7.7,8.2,Standard Twin Room,2 twin beds,53.604,Krabi,Thailand,2,0,0,0,0,0,0,2
+"Le Meridien Chiang Rai Resort, Thailand",Chiang Rai,9.0,9.4,"Deluxe Garden View, Guest room, 2 Twin/Single Bed(s)",2 twin beds,229.592,Chiang Rai,Thailand,2,0,0,0,0,0,0,2
+  Banpataraphan Khunyuam Maehongson Thailand,Ban Khun Yuam,7.1,0.0,Standard Twin Room,2 twin beds,38.17,Ban Khun Yuam,Thailand,2,0,0,0,0,0,0,2
+The Villager Fishermans Village,"Fisherman Village, Bophut",8.1,8.3,Standard Double Room,1 king bed,98.276,Bophut,Thailand,0,0,0,1,0,0,0,1
+The Villager Fishermans Village,"Fisherman Village, Bophut",8.1,8.3,Standard Double Room,1 king bed,98.276,Bophut,Thailand,0,0,0,1,0,0,0,1
+Debua Mahasarakham,Maha Sarakham,7.1,0.0,Deluxe Triple Room,"2 beds (1 twin, 1 full)",54.991,Maha Sarakham,Thailand,1,1,0,0,0,0,0,2
+Nara Residence Trang,Trang,9.0,9.6,Family Room,"4 beds (2 twins, 1 bunk bed, 1 futon)",164.239,Trang,Thailand,2,0,0,0,0,0,0,4
+Fern Resort Mae Hong Son,Mae Hong Son,8.9,8.7,Deluxe Double or Twin Room,   1 double or 2 twins,87.33800000000001,Mae Hong Son,Thailand,2,0,0,0,0,0,0,2
+Shanghai Mansion Bangkok - SHA Extra Plus,"Chinatown, Bangkok",8.5,8.8,Superior Double or Twin Room,   1 double or 2 twins,181.311,Bangkok,Thailand,2,0,0,0,0,0,0,2
+ibis Styles Bangkok Sukhumvit 4,"Khlong Toei, Bangkok",8.4,8.8,Standard Queen Room,1 full bed,108.41,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Sheraton Samui Resort,"Chaweng Noi Beachfront, Chaweng Noi Beach",8.4,9.0,"Deluxe, Guest room, 2 Twin/Single Bed(s)",2 twin beds,421.709,Chaweng Noi Beach,Thailand,2,0,0,0,0,0,0,2
+LANTA PURA beach resort-SHA extra plus,"Saladan, Ko Lanta",8.4,8.5,Deluxe Double or Twin Room,1 king bed,97.266,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Mercure Bangkok Sukhumvit 24,"Khlong Toei, Bangkok",8.5,9.0,Superior King Room with City View,1 king bed,187.505,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Mövenpick Asara Resort & Spa Hua Hin,Hua Hin,8.9,9.2,Villa with Private Pool,"2 beds (1 king, 1 sofa bed)",499.502,Hua Hin,Thailand,0,0,0,1,0,0,0,2
+Peach Blossom Resort & Pool Villa - SHA Plus,Karon Beach,7.6,0.0,Grand Deluxe Double or Twin Room,Multiple bed types,132.508,Phuket,Thailand,0,0,0,0,0,0,0,0
+Hansar Samui Resort & Spa - SHA Extra Plus,"Fisherman Village, Bophut",8.8,9.0,Deluxe Room,1 king bed,461.922,Bophut,Thailand,0,0,0,1,0,0,0,1
+Hansar Samui Resort & Spa - SHA Extra Plus,"Fisherman Village, Bophut",8.8,9.0,Deluxe Room,1 king bed,461.922,Bophut,Thailand,0,0,0,1,0,0,0,1
+99 The Gallery Hotel- SHA Extra Plus,"Si Phum, Chiang Mai",8.5,8.9,Standard Double or Twin Room,   1 double or 2 twins,63.969,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Vapa Hotel - SHA Extra Plus,Phuket,7.6,8.1,Superior Twin Room,2 twin beds,63.349000000000004,Phuket,Thailand,2,0,0,0,0,0,0,2
+,Ban Dan Chang,8.1,8.5,Superior Queen Room,1 queen bed,45.286,Ban Dan Chang,Thailand,0,0,1,0,0,0,0,1
+Saiyok River House,Sai Yok,7.5,0.0,Deluxe Room with River View,1 queen bed,51.756,Sai Yok,Thailand,0,0,1,0,0,0,0,1
+The Ember Hotel,"Khaosan, Bangkok",8.8,9.2,Standard Queen Room,1 full bed,156.747,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Hotel Loy Chiang Mai,"Chang Khlan, Chiang Mai",9.2,9.5,Deluxe Double Room,1 queen bed,258.541,Chiang Mai,Thailand,0,0,1,0,0,0,0,1
+MORA Boutique Hotel - SHA Extra Plus,Chiang Rai,9.3,9.6,Deluxe Double Room with City View-No Breakfast,1 king bed,165.859,Chiang Rai,Thailand,0,0,0,1,0,0,0,1
+Nakamanda Resort and Spa- SHA Plus,Klong Muang Beach,9.0,9.1,Sala Villa Lower Garden,"2 beds (1 sofa bed, 1 queen)",370.48900000000003,Klong Muang Beach,Thailand,0,0,1,0,0,0,0,2
+Nakamanda Resort and Spa- SHA Plus,Klong Muang Beach,9.0,9.1,Sala Villa Lower Garden,"2 beds (1 sofa bed, 1 queen)",370.48900000000003,Klong Muang Beach,Thailand,0,0,1,0,0,0,0,2
+Mercure Bangkok Siam,"Pathumwan, Bangkok",8.5,8.8,Superior Queen Room,"2 beds (1 full, 1 sofa bed)",201.072,Bangkok,Thailand,0,1,0,0,0,0,0,2
+Ibis Bangkok Sukhumvit 4,"Khlong Toei, Bangkok",7.9,8.4,Superior Twin Room,2 twin beds,76.377,Bangkok,Thailand,2,0,0,0,0,0,0,2
+ ,Seka,7.4,0.0,Superior Queen Room,1 queen bed,44.193,Seka,Thailand,0,0,1,0,0,0,0,1
+Shama Lakeview Asoke Bangkok,"Khlong Toei, Bangkok",8.5,9.0,Studio King,1 king bed,188.842,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Stay with Nimman Chiang Mai - SHA Extra Plus,"Nimmanhaemin, Chiang Mai",8.4,8.7,Superior King,1 full bed,99.32900000000001,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+Hilton Garden Inn Phuket Bang Tao - SHA Extra Plus,Bang Tao Beach,9.0,9.4,Twin Guest Room,2 twin beds,231.571,Bang Tao Beach,Thailand,2,0,0,0,0,0,0,2
+Hilton Garden Inn Phuket Bang Tao - SHA Extra Plus,Bang Tao Beach,9.0,9.4,Twin Guest Room,2 twin beds,231.571,Bang Tao Beach,Thailand,2,0,0,0,0,0,0,2
+Kesorn Boutique Residence at 8 Riew,Chachoengsao,7.8,0.0,Budget Double Room,1 full bed,36.229,Chachoengsao,Thailand,0,1,0,0,0,0,0,1
+Grand Mercure Bangkok Atrium,"Huai Khwang, Bangkok",7.8,8.4,Skyline Room with One King Bed - High Floor,1 king bed,124.214,Bangkok,Thailand,0,0,0,1,0,0,0,1
+The Krabi Forest Homestay,Ao Nang Beach,8.9,9.1,Single Room with Garden View,2 twin beds,44.543,Krabi,Thailand,2,0,0,0,0,0,0,2
+The Room at Maesai,Mae Sai,9.2,9.7,Loft Twin  Room,2 twin beds,64.339,Mae Sai,Thailand,2,0,0,0,0,0,0,2
+De Chai Oriental Nimman - SHA Plus,"Nimmanhaemin, Chiang Mai",8.7,9.1,Superior Double Room,1 king bed,163.566,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Punnpreeda Beach Resort - SHA Plus Certified,Bangrak Beach,7.6,8.0,Deluxe Villa with one way airport transfer,1 full bed,164.972,Bangrak Beach,Thailand,0,1,0,0,0,0,0,1
+ ,Ban Don Klang,8.8,9.3,Standard Twin Room with Mountain View,6 twin beds,46.742000000000004,Ban Don Klang,Thailand,6,0,0,0,0,0,0,6
+Ibis Phuket Patong,Patong Beach,7.7,0.0,Superior Twin Room,2 twin beds,80.336,Phuket,Thailand,2,0,0,0,0,0,0,2
+Jamahkiri Dive Resort & Spa,"Ao Thian Og, Ko Tao",8.6,9.1,Deluxe Room with Seaview,   1 double or 2 twins,335.12,Ko Tao,Thailand,2,0,0,0,0,0,0,2
+The Wood Pattani Hotel,Ban Ru Sa Mi Lae,8.7,9.6,Deluxe Double Room,1 king bed,84.10300000000001,Ban Ru Sa Mi Lae,Thailand,0,0,0,1,0,0,0,1
+River Resort & Spa,Sa Kaeo,7.2,0.0,Standard King Room,1 queen bed,51.821,Sa Kaeo,Thailand,0,0,1,0,0,0,0,1
+Palm Coco Mantra,"Thong Takian, Lamai",9.1,9.3,Villa (2 Adults),1 king bed,210.018,Lamai,Thailand,0,0,0,1,0,0,0,1
+Chalong Chalet Resort,Chalong,8.1,8.6,Double Room with Garden View,1 full bed,112.084,Phuket,Thailand,0,1,0,0,0,0,0,1
+Adelphi Pattaya - SHA Extra Plus,Pattaya,8.2,8.7,Deluxe Double or Twin Room with Balcony,   1 double or 2 twins,145.69400000000002,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Royal Muang Samui Villas - SHA Extra Plus,Choeng Mon Beach,8.6,8.9,One-Bedroom Family Pool Suite,"2 beds (1 twin, 1 full)",751.955,Choeng Mon Beach,Thailand,1,1,0,0,0,0,0,2
+Pullman Bangkok Hotel G,"Bang Rak, Bangkok",7.8,8.3,G Deluxe Room with Double Bed,1 full bed,194.085,Bangkok,Thailand,0,1,0,0,0,0,0,1
+The Fair House Beach Resort & Hotel - SHA Extra Plus,"Chaweng Noi Beachfront, Chaweng Noi Beach",8.0,8.4,Family Suite,"3 beds (2 twins, 1 full)",442.514,Chaweng Noi Beach,Thailand,2,1,0,0,0,0,0,3
+A Hotel Budget,Chiang Saen,7.6,0.0,Superior Double or Twin Room with City View,Multiple bed types,38.817,Chiang Rai,Thailand,0,0,0,0,0,0,0,0
+BANDER HOTEL,Phu Khieo,8.1,8.1,Standard Double Room,1 king bed,40.758,Phu Khieo,Thailand,0,0,0,1,0,0,0,1
+The Andaman Beach Hotel Phuket Patong - SHA Extra Plus,Patong Beach,8.0,8.5,Superior King Room,1 king bed,116.45100000000001,Phuket,Thailand,0,0,0,1,0,0,0,1
+Chang Buri Resort & Spa,"White Sand Beach, Ko Chang",8.2,8.6,Koh Chang Hillside Room,   1 double or 2 twins,120.785,Ko Chang,Thailand,2,0,0,0,0,0,0,2
+Celes Samui,Bophut,8.8,9.2,Standard Double or Twin Room,   1 double or 2 twins,272.366,Ko Samui,Thailand,2,0,0,0,0,0,0,2
+Millennium Hilton Bangkok,"Riverside, Bangkok",8.6,9.1,Family Suite,"2 beds (1 king, 1 sofa bed)",762.947,Bangkok,Thailand,0,0,0,1,0,0,0,2
+Sugar Marina Resort CLIFFHANGER Aonang - SHA Extra Plus,Ao Nang Beach,8.6,9.0,Deluxe Double or Twin Room with Cliff View,"2 beds (1 twin, 1 king)",126.74600000000001,Krabi,Thailand,1,0,0,1,0,0,0,2
+Malibu Beach Bungalows,Chaloklum,7.8,0.0,Bungalow with Garden View,1 king bed,131.007,Ko Phangan,Thailand,0,0,0,1,0,0,0,1
+Petchsiri Room ,Ban Chuat Plai Mai,7.8,8.6,Deluxe Room,1 full bed,43.9,Ban Chuat Plai Mai,Thailand,0,1,0,0,0,0,0,1
+Tropica Bungalow Beach Hotel - SHA Extra Plus,Patong Beach,7.9,8.3,Superior Bungalow,Multiple bed types,232.90200000000002,Phuket,Thailand,0,0,0,0,0,0,0,0
+Khum Lanna Boutique Hotel,Si Sa Ket,7.8,8.0,King Room with Garden View,1 king bed,64.048,Si Sa Ket,Thailand,0,0,0,1,0,0,0,1
+The Beach Samui - SHA Extra Plus,Taling Ngam Beach,9.0,9.6,Villa,2 king beds,388.17,Taling Ngam Beach,Thailand,0,0,0,2,0,0,0,2
+The Imperial Narathiwat Hotel,Narathiwat,6.0,0.0,Deluxe Double or Twin Room,   1 double or 2 twins,52.354,Narathiwat,Thailand,2,0,0,0,0,0,0,2
+Aquavana Haad Rin Resort,"Haad Rin Nok, Haad Rin",8.3,8.7,Deluxe Twin Room with Balcony,2 twin beds,121.99600000000001,Haad Rin,Thailand,2,0,0,0,0,0,0,2
+ Raibumrungphol,Mae Wang,8.1,0.0,Deluxe Dome,1 queen bed,139.935,Mae Wang,Thailand,0,0,1,0,0,0,0,1
+De Lanna Hotel,"Si Phum, Chiang Mai",8.4,8.7,Deluxe Twin Room,2 twin beds,137.153,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+The Riverie by Katathani SHA Extra Plus,Chiang Rai,9.0,9.5,Deluxe Twin Garden View,2 twin beds,214.418,Chiang Rai,Thailand,2,0,0,0,0,0,0,2
+Villa Cha-Cha Krabi Beachfront Resort,Ao Nam Mao,7.2,0.0,Deluxe Double Room with Balcony,1 full bed,133.983,Krabi,Thailand,0,1,0,0,0,0,0,1
+SALA Samui Choengmon Beach Resort - SHA Plus,Choeng Mon Beach,9.1,9.4,One-Bedroom Villa with Private Pool,1 queen bed,1144.347,Choeng Mon Beach,Thailand,0,0,1,0,0,0,0,1
+Sivana Villas Hua Hin,Hua Hin,8.3,8.7,2 Bedrooms Garden Pool Villa,"2 beds (1 king, 1 queen)",302.253,Hua Hin,Thailand,0,0,1,1,0,0,0,2
+"Fair House Villas & Spa, Koh Samui - SHA Extra Plus",Mae Nam,8.2,8.3,Sunset Suite,1 full bed,350.462,Ko Samui,Thailand,0,1,0,0,0,0,0,1
+NK Residence Sakon Nakhon,Sakon Nakhon,8.2,8.4,Standard Twin Room,2 twin beds,45.286,Sakon Nakhon,Thailand,2,0,0,0,0,0,0,2
+Tanote Villa SHA Extra Plus,Ko Tao,8.4,8.7,Deluxe Double or Twin Room with Garden View,Multiple bed types,149.963,Ko Tao,Thailand,0,0,0,0,0,0,0,0
+Aonang Silver Orchid Resort,Ao Nang Beach,7.9,8.3,Superior Double Room,   1 double or 2 twins,75.775,Krabi,Thailand,2,0,0,0,0,0,0,2
+GLOW Sukhumvit 5,"Wattana, Bangkok",7.9,8.4,Deluxe King Room,1 king bed,164.336,Bangkok,Thailand,0,0,0,1,0,0,0,1
+ZONE STATIONSthatphanom,That Phanom,8.1,8.7,Twin Room with Bathroom,2 twin beds,49.261,That Phanom,Thailand,2,0,0,0,0,0,0,2
+ZONE STATIONSthatphanom,That Phanom,8.1,8.7,Twin Room with Bathroom,2 twin beds,49.261,That Phanom,Thailand,2,0,0,0,0,0,0,2
+Nak Nakara Hotel-SHA Extra Plus,Chiang Rai,8.7,8.9,Executive Deluxe Twin Room,2 twin beds,108.688,Chiang Rai,Thailand,2,0,0,0,0,0,0,2
+Hop Inn Phetchabun,Phetchabun,8.7,9.1,Standard Double Room,1 queen bed,43.346000000000004,Phetchabun,Thailand,0,0,1,0,0,0,0,1
+Hop Inn Phetchabun,Phetchabun,8.7,9.1,Standard Double Room,1 queen bed,43.346000000000004,Phetchabun,Thailand,0,0,1,0,0,0,0,1
+Sabai Corner Bungalows - SHA Extra Plus,Ko Yao Noi,8.4,8.0,Bungalow with Garden View,1 queen bed,53.318,Ko Yao Noi,Thailand,0,0,1,0,0,0,0,1
+"Holiday Inn Pattaya, an IHG Hotel",Pattaya,8.6,9.0,Standard Room,   1 double or 2 twins,250.041,Pattaya,Thailand,2,0,0,0,0,0,0,2
+HOMA Phuket Town,Phuket,9.1,9.4,Studio Classic,1 queen bed,106.315,Phuket,Thailand,0,0,1,0,0,0,0,1
+Suporn Lakeview,Trakan Phut Phon,7.7,8.2,Deluxe Apartment,"2 beds (1 king, 1 sofa bed)",45.286,Trakan Phut Phon,Thailand,0,0,0,1,0,0,0,2
+Rimpao Hotel,Kalasin,6.6,0.0,Deluxe Double or Twin Room,"3 beds (2 twins, 1 full)",71.21000000000001,Kalasin,Thailand,2,1,0,0,0,0,0,3
+LG Lemon Grass,Na Jomtien,7.0,0.0,Deluxe Double Room with Balcony and Sea View,1 king bed,125.693,Pattaya,Thailand,0,0,0,1,0,0,0,1
+OYO 1044 Vassana Resort,Sattahip,7.3,0.0,Superior Double Room,1 king bed,46.036,Sattahip,Thailand,0,0,0,1,0,0,0,1
+Viang Thapae Resort- SHA Extra Plus,"Chang Moi, Chiang Mai",8.8,9.3,Superior Double Room,1 full bed,114.669,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+AMPM Hotel,Sungai Kolok,8.0,0.0,Double Room with Private Bathroom,1 king bed,46.429,Sungai Kolok,Thailand,0,0,0,1,0,0,0,1
+SO Bangkok,"Bang Rak, Bangkok",8.4,9.0,SO Cozy Double Room with City View,1 full bed,342.421,Bangkok,Thailand,0,1,0,0,0,0,0,1
+The Duchess Hotel - SHA Plus,"Pathumwan, Bangkok",8.1,8.5,Superior One-Bedroom,1 king bed,263.422,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Noom Guesthouse,Lop Buri,8.1,8.0,Double Room with Private Bathroom,1 full bed,35.582,Lop Buri,Thailand,0,1,0,0,0,0,0,1
+Pawapi Beach Resort Koh Mook,Ko Mook,7.7,0.0,Deluxe Bungalow with Sea View,1 queen bed,343.115,Ko Mook,Thailand,0,0,1,0,0,0,0,1
+"Holiday Inn Express Bangkok Sukhumvit 11, an IHG Hotel - Free Breakfast","Wattana, Bangkok",7.2,0.0,Standard Double or Twin Room,   1 double or 2 twins,128.84,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Chomduen Phu Kao Resort,Non Sang,8.1,8.2,Deluxe Queen Room,1 queen bed,32.347,Non Sang,Thailand,0,0,1,0,0,0,0,1
+The Opium Chiang Mai,"Chang Phueak, Chiang Mai",8.6,8.7,Studio,   1 double or 2 twins,64.69500000000001,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Sofitel Bangkok Sukhumvit,"Wattana, Bangkok",8.6,9.1,Luxury Twin Room,2 twin beds,402.67400000000004,Bangkok,Thailand,2,0,0,0,0,0,0,2
+SUhotel Suratthani,Suratthani,8.7,9.1,Standard King Room,1 king bed,36.848,Suratthani,Thailand,0,0,0,1,0,0,0,1
+"Chanalai Romantica Resort - Adults Only, Kata Beach - SHA Plus",Kata Beach,8.7,8.9,Superior Double or Twin Room,1 full bed,372.607,Kata Beach,Thailand,0,1,0,0,0,0,0,1
+"Chanalai Romantica Resort - Adults Only, Kata Beach - SHA Plus",Kata Beach,8.7,8.9,Superior Double or Twin Room,1 full bed,372.607,Kata Beach,Thailand,0,1,0,0,0,0,0,1
+Namkhong Guesthouse and Resort,Chiang Khong,7.9,8.0,Standard Twin Room with Fan and Shared Bathroom,2 twin beds,19.408,Chiang Rai,Thailand,2,0,0,0,0,0,0,2
+"Staybridge Suites Bangkok Thonglor, an IHG Hotel","Wattana, Bangkok",8.8,9.1,Studio Suite,2 twin beds,229.526,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Wisdom Hotel & Residence,Rayong,8.1,8.6,Deluxe Pool View,1 full bed,90.25,Rayong,Thailand,0,1,0,0,0,0,0,1
+Peeranon Resort,Ban Nong Khiam,8.5,9.1,King Room,1 king bed,42.052,Ban Nong Khiam,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok Silom Road Hotel,"Bang Rak, Bangkok",7.0,0.0,Superior Twin Room,2 twin beds,94.27,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Rimtarn Resort,Mae Hong Son,7.4,0.0,Standard Double or Twin Room,   1 double or 2 twins,64.69500000000001,Mae Hong Son,Thailand,2,0,0,0,0,0,0,2
+Crossroads house,Mae Hong Son,8.9,8.7,Budget Twin Room,2 twin beds,30.807000000000002,Mae Hong Son,Thailand,2,0,0,0,0,0,0,2
+The Nature Sukhothai,Sukhothai,8.3,9.0,Superior Double Room,1 queen bed,49.492000000000004,Sukhothai,Thailand,0,0,1,0,0,0,0,1
+Port Canary Airport Hotel,Lat Krabang,8.5,9.0,King Room with Balcony,1 king bed,87.616,Lat Krabang,Thailand,0,0,0,1,0,0,0,1
+Port Canary Airport Hotel,Lat Krabang,8.5,9.0,King Room with Balcony,1 king bed,87.616,Lat Krabang,Thailand,0,0,0,1,0,0,0,1
+"Interpark Hotel & Residence, Eastern Seaboard Rayong",Ban Phan Sadet Nok,7.7,8.0,Standard Twin Room,2 twin beds,113.21600000000001,Ban Phan Sadet Nok,Thailand,2,0,0,0,0,0,0,2
+Buri Tara Resort - SHA Extra Plus,Ao Nang Beach,8.1,8.3,Superior Double Room with Balcony,   1 double or 2 twins,90.111,Krabi,Thailand,2,0,0,0,0,0,0,2
+Mercure Bangkok Makkasan,Bangkok,8.1,9.1,Superior King Room,1 king bed,135.921,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Furama Silom Hotel,"Bang Rak, Bangkok",7.2,0.0,Deluxe Double or Twin Room,   1 double or 2 twins,99.638,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Sappraiwan Elephant Resort&Sanctuary,Kaengsopha,7.9,0.0,Superior Room,   1 double or 2 twins,258.78000000000003,Kaengsopha,Thailand,2,0,0,0,0,0,0,2
+Grande Centre Point Pattaya,Pattaya,8.8,9.2,Panoramic Suite,1 king bed,613.647,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Wild Hippie Chang,Ban Nong Si Chaeng,9.6,8.0,Bungalow with Fan,1 queen bed,35.49,Ban Nong Si Chaeng,Thailand,0,0,1,0,0,0,0,1
+Na Nirand Romantic Boutique Resort,"Chang Khlan, Chiang Mai",9.2,9.4,Riverfront Colonial Suite,1 queen bed,1618.151,Chiang Mai,Thailand,0,0,1,0,0,0,0,1
+MELIÁ PHUKET MAI KHAO - SHA Plus,Mai Khao Beach,8.9,9.5,One-Bedroom Wellness Villa with Private Pool,1 king bed,963.398,Mai Khao Beach,Thailand,0,0,0,1,0,0,0,1
+MELIÁ PHUKET MAI KHAO - SHA Plus,Mai Khao Beach,8.9,9.5,One-Bedroom Wellness Villa with Private Pool,1 king bed,963.398,Mai Khao Beach,Thailand,0,0,0,1,0,0,0,1
+Lipe Beach Resort,"Ko Lipe Sunrise Beach, Ko Lipe",8.0,8.1,Garden Room Fan-No Hot Shower,1 queen bed,124.214,Ko Lipe,Thailand,0,0,1,0,0,0,0,1
+Pupiang po Da Arte Resort,Dan Sai,9.2,9.6,Deluxe Room,1 king bed,157.209,Dan Sai,Thailand,0,0,0,1,0,0,0,1
+Oon Valley Farm Stay,Ban Mae Pha Haen,7.9,8.4,Quadruple Room,4 twin beds,117.745,Ban Mae Pha Haen,Thailand,4,0,0,0,0,0,0,4
+The Royal Paradise Hotel & Spa - SHA Extra Plus,Patong Beach,7.8,8.2,Deluxe Double or Twin - Paradise Wing,Multiple bed types,158.603,Phuket,Thailand,0,0,0,0,0,0,0,0
+Novotel Phuket Resort,Patong Beach,7.7,8.0,Superior Twin Room,2 twin beds,154.282,Phuket,Thailand,2,0,0,0,0,0,0,2
+Amari Koh Samui,"Chaweng Beachfront, Chaweng",8.4,8.7,Superior King Room - Garden Wing,1 king bed,269.067,Chaweng,Thailand,0,0,0,1,0,0,0,1
+Frankie's Inn - SHA Extra Plus,Jomtien Beach,7.8,8.7,Deluxe King Studio,1 king bed,76.386,Pattaya,Thailand,0,0,0,1,0,0,0,1
+OYO 854 Baan Phuchalong,Phuket,7.4,0.0,Superior Double Room,1 king bed,37.480000000000004,Phuket,Thailand,0,0,0,1,0,0,0,1
+Conrad Bangkok,"Pathumwan, Bangkok",8.7,9.1,Grand Premium King,1 king bed,541.217,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Ok Farm Resort,Det Udom,8.9,9.2,Queen Room with Garden View,1 king bed,48.521,Det Udom,Thailand,0,0,0,1,0,0,0,1
+S.S.Hotel Nangrong,Nang Rong,8.1,8.7,Standard Double Room,1 king bed,40.481,Nang Rong,Thailand,0,0,0,1,0,0,0,1
+Gudi Boutique Hotel,Mae Rim,7.8,8.1,Deluxe Double or Twin Room,Multiple bed types,60.861000000000004,Mae Rim,Thailand,0,0,0,0,0,0,0,0
+Gudi Boutique Hotel,Mae Rim,7.8,8.1,Deluxe Double or Twin Room,Multiple bed types,60.861000000000004,Mae Rim,Thailand,0,0,0,0,0,0,0,0
+Kept Bangsaray Hotel Pattaya,Bang Sare,8.7,8.9,Deluxe Double Room,1 king bed,625.015,Bang Sare,Thailand,0,0,0,1,0,0,0,1
+Novotel Bangkok on Siam Square,"Pathumwan, Bangkok",7.3,0.0,Executive Double Room,1 king bed,336.793,Bangkok,Thailand,0,0,0,1,0,0,0,1
+ASAI Bangkok Chinatown,"Chinatown, Bangkok",8.9,9.4,Roomy Queen Courtyard,1 king bed,160.197,Bangkok,Thailand,0,0,0,1,0,0,0,1
+El Pillax Lanta Resort,"Klong Khong Beach, Ko Lanta",8.4,9.1,Deluxe Bungalow,1 king bed,420.517,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Six Senses Samui,Choeng Mon Beach,9.0,9.3,Ocean View Pool Villa,1 queen bed,1485.944,Choeng Mon Beach,Thailand,0,0,1,0,0,0,0,1
+Sinsiri hostel,Ban Ku,8.2,0.0,Standard Twin Room,2 twin beds,40.176,Ban Ku,Thailand,2,0,0,0,0,0,0,2
+Novotel Bangkok Ploenchit Sukhumvit,"Pathumwan, Bangkok",8.0,8.4,Deluxe Twin Room,2 twin beds,222.582,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Royal Cliff Grand Hotel - SHA Extra Plus,Pattaya South,8.4,8.8,Grand Sea View,Multiple bed types,288.03000000000003,Pattaya,Thailand,0,0,0,0,0,0,0,0
+SYN Boutique Hotel,Chiang Mai,8.9,9.4,Stylistic Twin Room,2 twin beds,258.133,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+The Beach Village Resort,Sam Roi Yot,8.4,8.7,One-Bedroom Garden Suite,1 full bed,147.311,Sam Roi Yot,Thailand,0,1,0,0,0,0,0,1
+The Beach Village Resort,Sam Roi Yot,8.4,8.7,One-Bedroom Garden Suite,1 full bed,147.311,Sam Roi Yot,Thailand,0,1,0,0,0,0,0,1
+Phi Phi Relax Beach Resort,"Pak Nam Bay, Phi Phi Islands",8.1,8.1,Bungalow with Balcony,1 king bed,209.612,Phi Phi Islands,Thailand,0,0,0,1,0,0,0,1
+Funwan Hotel,Li,8.2,8.7,Standard King Room,1 king bed,45.416000000000004,Li,Thailand,0,0,0,1,0,0,0,1
+The Quartier Boutique Hotel Sukhumvit 39 Bangkok by Compass Hospitality,"Wattana, Bangkok",8.2,8.9,Premier Double Room,1 full bed,150.77700000000002,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Baan Bua Guest House,Chiang Rai,8.2,8.0,Double or Twin Room with Fan,   1 double or 2 twins,28.53,Chiang Rai,Thailand,2,0,0,0,0,0,0,2
+Pajamas Koh Chang,"Klong Prao Beach, Ko Chang",8.9,9.1,Double Room with Garden View,1 queen bed,122.69800000000001,Ko Chang,Thailand,0,0,1,0,0,0,0,1
+DoubleTree by Hilton Bangkok Ploenchit - SHA Plus Certified,"Khlong Toei, Bangkok",8.5,8.9,Twin Guest Room,2 twin beds,217.827,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Thames Tara Pool Villa Rawai Phuket - SHA Extra Plus,Rawai Beach,8.0,8.2,3 Bedrooms Pool Villa,"6 beds (4 twins, 1 king, 1 sofa bed)",349.353,Phuket,Thailand,4,0,0,1,0,0,0,6
+ - Me Vadsana Hotel,Ban Thung Yao,9.0,8.1,Standard Family Room,"2 beds (1 twin, 1 full)",51.109,Ban Thung Yao,Thailand,1,1,0,0,0,0,0,2
+Islanda Hideaway Resort - SHA Extra Plus,"Ko Klang, Krabi",8.8,9.2,Garden View Bungalow Triple,"2 beds (1 full, 1 sofa bed)",175.919,Krabi,Thailand,0,1,0,0,0,0,0,2
+Park Villa Chaiyaphume,Chaiyaphum,8.5,9.0,Double Room,1 king bed,60.166000000000004,Chaiyaphum,Thailand,0,0,0,1,0,0,0,1
+COSI Pattaya Wong Amat Beach - SHA Plus Certified,North Pattaya,8.6,8.9,COSI Room King,1 king bed,90.788,Pattaya,Thailand,0,0,0,1,0,0,0,1
+"Phuket Marriott Resort and Spa, Nai Yang Beach",Nai Yang Beach,8.7,9.3,"Premium Pool Access, Guest room, 2 Double",2 queen beds,403.128,Nai Yang Beach,Thailand,0,0,2,0,0,0,0,2
+Twin Bay Resort - SHA Extra Plus,"Ko Kwang Beach, Ko Lanta",8.6,8.8,Deluxe Family Bungalow,"2 beds (1 bunk bed, 1 queen)",203.78900000000002,Ko Lanta,Thailand,0,0,1,0,0,0,0,2
+Gallery Design - SHA Extra Plus,Si Sa Ket,8.0,8.6,Deluxe Double Room,1 king bed,113.21600000000001,Si Sa Ket,Thailand,0,0,0,1,0,0,0,1
+Moracea by Khao Lak Resort - SHA Extra Plus,"Khao Lak Beach, Khao Lak",8.8,9.2,Grand Deluxe Double or Twin Room,   1 double or 2 twins,188.684,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+Eden Bungalows Fisherman's village,"Fisherman Village, Bophut",8.9,9.3,Classic Double Room,1 king bed,122.92,Bophut,Thailand,0,0,0,1,0,0,0,1
+Eden Bungalows Fisherman's village,"Fisherman Village, Bophut",8.9,9.3,Classic Double Room,1 king bed,122.92,Bophut,Thailand,0,0,0,1,0,0,0,1
+Anantara Riverside Bangkok Resort,"Thonburi, Bangkok",8.9,9.2,Deluxe Room,Multiple bed types,494.88,Bangkok,Thailand,0,0,0,0,0,0,0,0
+ Lung Bun Camp,Ban Nong Rang Chang,9.1,9.2,Tent,1 queen bed,110.046,Ban Nong Rang Chang,Thailand,0,0,1,0,0,0,0,1
+Centara Koh Chang Tropicana Resort,"Klong Prao Beach, Ko Chang",8.3,8.6,Deluxe Twin Room,2 twin beds,240.67600000000002,Ko Chang,Thailand,2,0,0,0,0,0,0,2
+Mercure Bangkok Sukhumvit 11,"Wattana, Bangkok",8.4,9.0,Deluxe King Room,1 full bed,215.85,Bangkok,Thailand,0,1,0,0,0,0,0,1
+JP Resort Koh Tao,"Chalok Baan Kao, Ko Tao",7.8,0.0,Standard Bungalow with Sea View,1 full bed,45.139,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Arcadia Maephim,Mae Pim,7.2,0.0,Deluxe Executive Suite with Sea View-2 Bedrooms,"2 beds (1 full, 1 queen)",288.863,Mae Pim,Thailand,0,1,1,0,0,0,0,2
+Santhiya Tree Koh Chang Resort,"Klong Prao Beach, Ko Chang",8.8,9.0,Villa Pool Access,1 queen bed,334.615,Ko Chang,Thailand,0,0,1,0,0,0,0,1
+Diamond Cottage Resort & Spa - SHA Extra Plus,Karon Beach,0.0,0.0,Deluxe Double or Twin Room with Pool View,   1 double or 2 twins,151.727,Phuket,Thailand,2,0,0,0,0,0,0,2
+Ban Chomdoi Resort PhaTang,Ban Pha Tang,8.3,8.2,Tent,1 futon bed,25.878,Ban Pha Tang,Thailand,0,0,0,0,0,0,0,0
+Surin Sunset Hotel - SHA,Surin Beach,7.0,0.0,Double Room with Balcony and Sea View,1 queen bed,124.764,Phuket,Thailand,0,0,1,0,0,0,0,1
+Novotel Phuket Vintage Park Resort,Patong Beach,8.0,8.2,Superior Twin Room,2 twin beds,148.367,Phuket,Thailand,2,0,0,0,0,0,0,2
+Happy Land Residence,Ban Nam Cham,10.0,10.0,Double Room,1 full bed,87.8,Ban Nam Cham,Thailand,0,1,0,0,0,0,0,1
+Asia Hotel Bangkok,"Phaya Thai, Bangkok",7.6,0.0,Executive Double or Twin Room,2 twin beds,117.375,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Rawai Palm Beach Resort - SHA Extra Plus,Rawai Beach,7.7,0.0,Deluxe Family Room with Pool View,   1 double or 2 twins,245.919,Phuket,Thailand,2,0,0,0,0,0,0,2
+COSI Samui Chaweng Beach - SHA Plus,Chaweng,8.2,8.8,COSI - KING Room with daily food & drink credit,1 king bed,119.35300000000001,Chaweng,Thailand,0,0,0,1,0,0,0,1
+L'esprit De Naiyang Beach Resort - SHA Extra Plus,Nai Yang Beach,8.3,8.7,One Bedroom Garden Villa (FREE One Way Airport Transfer),"2 beds (1 king, 1 sofa bed)",148.13,Nai Yang Beach,Thailand,0,0,0,1,0,0,0,2
+Le Meridien Chiang Mai,"Chang Moi, Chiang Mai",8.6,9.2,"Urban Room (K), Guest room, 1 King, Sofa bed","2 beds (1 king, 1 sofa bed)",309.633,Chiang Mai,Thailand,0,0,0,1,0,0,0,2
+The Lord Nelson Hotel,"Phala Beach, Ban Chang",7.9,8.3,Deluxe Family Suite,2 king beds,332.767,Ban Chang,Thailand,0,0,0,2,0,0,0,2
+Central Park Hotel,Sing Buri,7.4,0.0,Standard Twin Room,2 twin beds,32.347,Sing Buri,Thailand,2,0,0,0,0,0,0,2
+Hotel MYS Khao Yai,Mu Si,10.0,10.0,Suite,1 king bed,624.468,Mu Si,Thailand,0,0,0,1,0,0,0,1
+Hotel MYS Khao Yai,Mu Si,10.0,10.0,Suite,1 king bed,624.468,Mu Si,Thailand,0,0,0,1,0,0,0,1
+LAKE HOUSE Naka Cave,Ban Don Klang,8.6,9.1,Small Double Room,1 queen bed,129.39000000000001,Ban Don Klang,Thailand,0,0,1,0,0,0,0,1
+Neenlawat Riverside,Suratthani,8.6,8.6,Standard Double Room,1 queen bed,64.048,Suratthani,Thailand,0,0,1,0,0,0,0,1
+   Deetorjai Resort,Chiang Muan,8.8,9.5,Standard Queen Room,1 queen bed,40.887,Chiang Muan,Thailand,0,0,1,0,0,0,0,1
+Wellness Stay & Hotel Sukhumvit 107,Bangna,9.1,9.4,Superior Double Room,1 full bed,109.24600000000001,Bangna,Thailand,0,1,0,0,0,0,0,1
+Racha Island Resort (Rayaburi),Ko Racha Yai,7.7,8.0,Chino Pool View,   1 double or 2 twins,248.519,Ko Racha Yai,Thailand,2,0,0,0,0,0,0,2
+Phuruarounmai Organic Living Resort,Loei,8.1,8.2,Deluxe Double Room with Balcony,1 queen bed,51.821,Loei,Thailand,0,0,1,0,0,0,0,1
+Phuruarounmai Organic Living Resort,Loei,8.1,8.2,Deluxe Double Room with Balcony,1 queen bed,51.821,Loei,Thailand,0,0,1,0,0,0,0,1
+Pastell Oldtown Chiang Mai SHA Extra Plus,"Phra Sing, Chiang Mai",8.9,9.2,Deluxe Double Room,1 king bed,142.162,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Midtown Sukhothai,Sukhothai,8.8,9.2,Superior Twin Room,2 twin beds,82.024,Sukhothai,Thailand,2,0,0,0,0,0,0,2
+SO Sofitel Hua Hin,Cha Am,8.3,8.8,Two-Bedroom Villa with Private Pool,"3 beds (2 twins, 1 king)",1246.6970000000001,Cha Am,Thailand,2,0,0,1,0,0,0,3
+Beyond Resort Krabi,Klong Muang Beach,8.2,8.2,Villa with Garden View,   1 double or 2 twins,283.65500000000003,Klong Muang Beach,Thailand,2,0,0,0,0,0,0,2
+Beyond Resort Krabi,Klong Muang Beach,8.2,8.2,Villa with Garden View,   1 double or 2 twins,283.65500000000003,Klong Muang Beach,Thailand,2,0,0,0,0,0,0,2
+MUTHI MAYA Forest Pool Villa Resort - SHA Plus Certified,Mu Si,9.0,9.2,One-Bedroom Villa with Private Pool,"2 beds (1 sofa bed, 1 queen)",576.543,Mu Si,Thailand,0,0,1,0,0,0,0,2
+MUTHI MAYA Forest Pool Villa Resort - SHA Plus Certified,Mu Si,9.0,9.2,One-Bedroom Villa with Private Pool,"2 beds (1 sofa bed, 1 queen)",576.543,Mu Si,Thailand,0,0,1,0,0,0,0,2
+HR Resort,Ban Dong Bang,9.5,9.7,Deluxe Double Room with Shower,1 king bed,87.33800000000001,Ban Dong Bang,Thailand,0,0,0,1,0,0,0,1
+Avani Plus Koh Lanta Krabi Resort - SHA Extra Plus,"Ko Kwang Beach, Ko Lanta",8.9,9.5,Pool View Room,1 king bed,268.847,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+Seashell Resort Koh Tao-SHA Plus,"Sairee Beach, Ko Tao",7.2,0.0,Bungalow with Fan,1 full bed,102.833,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Baan Sawan Samui Resort,Chaweng Noi Beach,9.5,9.7,Two-Bedroom Villa,"3 beds (2 twins, 1 king)",255.281,Chaweng,Thailand,2,0,0,1,0,0,0,3
+Wonderland Healing Center,Thong Sala,9.1,9.1,King Suite,1 king bed,363.909,Thong Sala,Thailand,0,0,0,1,0,0,0,1
+Lanta Seafront Resort,"Klong Nin Beach, Ko Lanta",8.0,8.2,Deluxe Double Room,1 full bed,75.32300000000001,Ko Lanta,Thailand,0,1,0,0,0,0,0,1
+Holatel,Phitsanulok,7.9,8.3,Deluxe Double Room,1 twin bed,79.35300000000001,Phitsanulok,Thailand,1,0,0,0,0,0,0,1
+The Landmark Bangkok - SHA Extra Plus,"Khlong Toei, Bangkok",8.7,9.1,Deluxe Suite,1 queen bed,618.806,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Le Meridien Khao Lak Resort & Spa,"Bangsak Beach, Khao Lak",8.9,9.3,"Deluxe Pool View Twin, Deluxe Guest room, Pool view, Balcony",2 twin beds,187.79500000000002,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+Le Meridien Khao Lak Resort & Spa,"Bangsak Beach, Khao Lak",8.9,9.3,"Deluxe Pool View Twin, Deluxe Guest room, Pool view, Balcony",2 twin beds,187.79500000000002,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+ ,Ban Don Klang,8.9,9.8,Double Room with Terrace,1 full bed,46.58,Ban Don Klang,Thailand,0,1,0,0,0,0,0,1
+The Elysium Residence - SHA Extra Plus,Chalong,8.9,9.3,Standard Double Room with Sea View,1 queen bed,85.028,Phuket,Thailand,0,0,1,0,0,0,0,1
+The Desiign Hotel - SHA,"304 Industrial Park, Si Maha Phot",8.8,9.0,Superior Double Room,1 king bed,95.749,Prachin Buri,Thailand,0,0,0,1,0,0,0,1
+David Residence- SHA Extra Plus,Nai Yang Beach,8.3,8.7,Deluxe Double Room (Free Round Trip Airport Transfer),1 queen bed,103.641,Nai Yang Beach,Thailand,0,0,1,0,0,0,0,1
+Sukorn Andaman Beach Resort,Ko Sukon,9.0,8.6,Deluxe Bungalow with Fan,   1 double or 2 twins,51.756,Ko Sukon,Thailand,2,0,0,0,0,0,0,2
+Naga Tara Boutique Resort,Phayao,8.2,8.5,Standard Double Room,1 king bed,64.69500000000001,Phayao,Thailand,0,0,0,1,0,0,0,1
+Gate43 Airport Hotel,Lat Krabang,8.2,8.7,Triple Room with Lake View,3 twin beds,178.246,Lat Krabang,Thailand,3,0,0,0,0,0,0,3
+Gate43 Airport Hotel,Lat Krabang,8.2,8.7,Triple Room with Lake View,3 twin beds,178.246,Lat Krabang,Thailand,3,0,0,0,0,0,0,3
+Railay Princess Resort & Spa-SHA Extra Plus,Railay Beach,7.8,8.5,Superior Double or Twin Room,   1 double or 2 twins,139.593,Railay Beach,Thailand,2,0,0,0,0,0,0,2
+BA? Apartment? Flow?Suvarnabhumi,Bang Phli,8.1,8.4,Loft,1 king bed,43.669000000000004,Bang Phli,Thailand,0,0,0,1,0,0,0,1
+Chatrium Grand Bangkok,"Pratunam, Bangkok",9.2,9.5,Premier Room,Multiple bed types,611.46,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Fanari Khaolak Resort - Courtyard SHA Extra Plus,"Bang Niang Beach, Khao Lak",7.9,0.0,Deluxe Double Room with Pool View,1 queen bed,69.621,Phang Nga,Thailand,0,0,1,0,0,0,0,1
+Haadson Resort & Koh Raham,Haad Son,7.5,0.0,Deluxe Double or Twin Room with Balcony,   1 double or 2 twins,174.67600000000002,Haad Son,Thailand,2,0,0,0,0,0,0,2
+Phi Phi Holiday Resort-SHA Extra Plus,"Laem Tong Bay, Phi Phi Islands",7.9,8.0,Coral Beach Studio,1 full bed,463.475,Phi Phi Islands,Thailand,0,1,0,0,0,0,0,1
+The Hi Place,Roi Et,8.7,8.9,King Suite with Balcony,"3 beds (1 king, 2 sofa beds)",203.78900000000002,Roi Et,Thailand,0,0,0,1,2,0,0,3
+Panya Garden Resort,Ban Huai Na,7.1,0.0,Double Room with Balcony,"2 beds (1 twin, 1 queen)",38.817,Ban Huai Na,Thailand,1,0,1,0,0,0,0,2
+Diamond Beach Resort,Ao Nam Mao,7.3,0.0,Superior Double Room,   1 double or 2 twins,83.179,Krabi,Thailand,2,0,0,0,0,0,0,2
+Novotel Bangkok Sukhumvit 20,"Khlong Toei, Bangkok",8.5,9.1,Executive Floor Deluxe King Room,1 full bed,276.771,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Hilton Sukhumvit Bangkok,"Khlong Toei, Bangkok",8.5,9.1,Twin Deluxe Room,2 twin beds,331.243,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Sawadeelanna Hotel,Nan,8.4,8.7,Double Room with Mountain View,1 king bed,69.871,Nan,Thailand,0,0,0,1,0,0,0,1
+Buasri Boutique Patong,"Nanai Road, Patong Beach",8.4,8.7,King Room with Pool View,1 king bed,83.913,Patong Beach,Thailand,0,0,0,1,0,0,0,1
+Le Patta Hotel Chiang Rai SHA Extra Plus,Chiang Rai,9.1,9.6,Deluxe Double Room,1 king bed,184.219,Chiang Rai,Thailand,0,0,0,1,0,0,0,1
+JADAE HOUSE,Ban Huai Khai,9.7,10.0,Double Room with Mountain View,1 king bed,136.927,Ban Huai Khai,Thailand,0,0,0,1,0,0,0,1
+kwanlah homestay,Mae Chaem,8.5,8.4,Double Room with Terrace,"3 beds (1 twin, 2 fulls)",77.634,Mae Chaem,Thailand,1,2,0,0,0,0,0,3
+Ibis Phuket Kata,Kata Beach,7.9,8.2,Standard Twin Room,2 twin beds,94.35000000000001,Kata Beach,Thailand,2,0,0,0,0,0,0,2
+Ibis Phuket Kata,Kata Beach,7.9,8.2,Standard Twin Room,2 twin beds,94.35000000000001,Kata Beach,Thailand,2,0,0,0,0,0,0,2
+"Hotel Indigo Phuket Patong, an IHG Hotel - SHA Extra Plus",Patong Beach,8.8,9.3,Standard Room,Multiple bed types,259.549,Phuket,Thailand,0,0,0,0,0,0,0,0
+Avista Hideaway Phuket Patong - MGallery,"Tri Trang Beach, Patong Beach",8.0,8.4,"Deluxe Room King Bed, Garden View",1 queen bed,264.556,Patong Beach,Thailand,0,0,1,0,0,0,0,1
+Loei Palace Hotel,Loei,7.7,0.0,Deluxe King Room,1 king bed,63.239000000000004,Loei,Thailand,0,0,0,1,0,0,0,1
+Loei Palace Hotel,Loei,7.7,0.0,Deluxe King Room,1 king bed,63.239000000000004,Loei,Thailand,0,0,0,1,0,0,0,1
+Karin hotel & Service apartment - SHA Extra Plus,Si Racha,8.6,8.8,Premium Studio,1 king bed,146.488,Si Racha,Thailand,0,0,0,1,0,0,0,1
+Karin hotel & Service apartment - SHA Extra Plus,Si Racha,8.6,8.8,Premium Studio,1 king bed,146.488,Si Racha,Thailand,0,0,0,1,0,0,0,1
+Centara Anda Dhevi Resort and Spa - SHA Plus,Ao Nang Beach,8.3,8.5,Deluxe King Room,1 king bed,114.21900000000001,Krabi,Thailand,0,0,0,1,0,0,0,1
+Panphuree Residence - SHA Extra Plus,Nai Yang Beach,8.3,8.9,Deluxe Double Room,1 king bed,84.10300000000001,Nai Yang Beach,Thailand,0,0,0,1,0,0,0,1
+"Kacha Resort & Spa, Koh Chang - SHA Extra Plus","White Sand Beach, Ko Chang",8.0,8.4,Hillside Deluxe Building,Multiple bed types,169.686,Ko Chang,Thailand,0,0,0,0,0,0,0,0
+Maitria Mode Sukhumvit 15 Bangkok - A Chatrium Collection,"Wattana, Bangkok",8.7,9.1,Deluxe King Room,1 king bed,214.048,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Watermill Resort,Nong Nam Daeng,8.4,8.7,Villa with Creek View,1 queen bed,143.526,Nong Nam Daeng,Thailand,0,0,1,0,0,0,0,1
+Phi Phi Harbour View Hotel-SHA Extra Plus,"Tonsai Bay, Phi Phi Islands",7.3,0.0,Deluxe Triple Room,"2 beds (1 twin, 1 full)",346.372,Phi Phi Islands,Thailand,1,1,0,0,0,0,0,2
+Baan Suan Chiva Saran,Nong Bua Lamphu,7.0,8.2,Triple Room,"2 beds (1 twin, 1 queen)",35.582,Nong Bua Lamphu,Thailand,1,0,1,0,0,0,0,2
+"@ Border Hotel, Aranyaprateth",Aranyaprathet,7.6,8.2,Standard Double Room,1 full bed,66.543,Aranyaprathet,Thailand,0,1,0,0,0,0,0,1
+Darley Hotel Chiangmai,"Chang Moi, Chiang Mai",8.6,8.9,Superior Twin Room,2 twin beds,75.299,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Oakwood Suites Bangkok,"Khlong Toei, Bangkok",9.0,9.4,Premier Apartment,1 queen bed,396.322,Bangkok,Thailand,0,0,1,0,0,0,0,1
+At Chiang Mai - SHA Extra Plus,"Si Phum, Chiang Mai",8.5,8.8,Superior King or Twin Room,   1 double or 2 twins,97.819,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Samui Mermaid Beachfront,Bangrak Beach,7.3,0.0,Standard Double Room with Fan,1 queen bed,51.109,Bangrak Beach,Thailand,0,0,1,0,0,0,0,1
+Royal Nakhara Hotel and Convention Centre,Nong Khai,7.5,0.0,Deluxe Double Room,1 queen bed,90.57300000000001,Nong Khai,Thailand,0,0,1,0,0,0,0,1
+The Beachfront Hotel Phuket,Rawai Beach,7.5,8.3,Superior Double or Twin Room,1 full bed,116.45100000000001,Phuket,Thailand,0,1,0,0,0,0,0,1
+M CASA HOTEL PATTAYA,Nong Prue,7.9,8.1,Deluxe Room,1 twin bed,85.397,Nong Prue,Thailand,1,0,0,0,0,0,0,1
+M CASA HOTEL PATTAYA,Nong Prue,7.9,8.1,Deluxe Room,1 twin bed,85.397,Nong Prue,Thailand,1,0,0,0,0,0,0,1
+PAN KLED VILLA eco hill resort - SHA extra plus,Chiang Rai,8.8,9.0,Classic Double,1 full bed,80.22200000000001,Chiang Rai,Thailand,0,1,0,0,0,0,0,1
+Mook Lamai Resort and Spa,Ko Mook,9.3,9.4,One-Bedroom Villa with  Pool View,1 queen bed,323.475,Ko Mook,Thailand,0,0,1,0,0,0,0,1
+Chour Palace Hotel,Mae Sai,8.4,8.6,Deluxe Double Room,1 full bed,52.403,Mae Sai,Thailand,0,1,0,0,0,0,0,1
+Phayamas Private Beach Resort and Island Brew,Ko Phayam,9.1,9.3,Double Bungalow with Fan,1 king bed,127.181,Ko Phayam,Thailand,0,0,0,1,0,0,0,1
+Montien Hotel Surawong Bangkok,"Bang Rak, Bangkok",8.9,9.3,Deluxe Twin Room,2 twin beds,273.07800000000003,Bangkok,Thailand,2,0,0,0,0,0,0,2
+SeaRidge Hua Hin Resort & Poolvilla,Khao Tao,7.9,8.3,Superior Two-Bedroom Suite with Balcony,"3 beds (2 twins, 1 queen)",417.283,Khao Tao,Thailand,2,0,1,0,0,0,0,3
+By Beach Resort,Ban Bang Po,7.8,0.0,Bungalow with Air Conditioning,1 queen bed,74.936,Ban Bang Po,Thailand,0,0,1,0,0,0,0,1
+Hilltop Wellness Resort,Phuket,8.3,9.0,Superior Double or Twin Room with Pool View,Multiple bed types,162.449,Phuket,Thailand,0,0,0,0,0,0,0,0
+Villa San Pee Seua - SHA extra plus,"San Phi Suea, Chiang Mai",9.3,9.3,Family Suite,   1 double or 2 twins,209.612,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Lamai Inn 99 Bungalows,Lamai,8.2,8.3,Standard Double Room,1 king bed,76.418,Lamai,Thailand,0,0,0,1,0,0,0,1
+Lamai Inn 99 Bungalows,Lamai,8.2,8.3,Standard Double Room,1 king bed,76.418,Lamai,Thailand,0,0,0,1,0,0,0,1
+"Holiday Inn Resort Krabi Ao Nang Beach, an IHG Hotel",Ao Nang Beach,8.3,8.7,Standard Room,2 twin beds,126.183,Krabi,Thailand,2,0,0,0,0,0,0,2
+Brighton Grand Hotel Pattaya - SHA Extra Plus,North Pattaya,8.5,8.9,Grand Deluxe Harbor View,Multiple bed types,198.732,Pattaya,Thailand,0,0,0,0,0,0,0,0
+Alcove Bungalow,Hinkong,9.1,9.1,Deluxe Bungalow with Garden View,1 king bed,172.828,Hinkong,Thailand,0,0,0,1,0,0,0,1
+Diamond Cave Resort,Railay Beach,6.8,0.0,Standard Queen Room with Two Queen Beds,2 full beds,96.072,Railay Beach,Thailand,0,2,0,0,0,0,0,2
+Armthong Home,Ban Nong Toei,8.6,9.2,Villa with Garden View,"8 beds (4 twins, 3 kings, 1 sofa bed)",155.268,Ban Nong Toei,Thailand,4,0,0,3,0,0,0,8
+,Ban Mae Khachan,8.8,8.7,Deluxe Room,1 king bed,38.817,Ban Mae Khachan,Thailand,0,0,0,1,0,0,0,1
+Lomsabai Apartments,Bangsaen,7.3,0.0,Deluxe Double Room,1 queen bed,55.314,Chon Buri,Thailand,0,0,1,0,0,0,0,1
+Koh Yao Yai Hillside Resort,Ko Yao Yai,9.0,9.4,Grand Superior Room,"2 beds (1 king, 1 sofa bed)",175.601,Ko Yao Yai,Thailand,0,0,0,1,0,0,0,2
+Hotel Muse Bangkok Langsuan - MGallery,"Pathumwan, Bangkok",8.0,8.4,Jatu Deluxe,1 king bed,271.811,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Hotel De Sripoom -SHA Extra Plus,"Si Phum, Chiang Mai",8.8,9.0,Deluxe Double Room with Bath,1 king bed,147.311,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+The Sarann - SHA Extra Plus,Chaweng Noi Beach,7.7,8.2,Deluxe Double or Twin Room,2 twin beds,155.477,Chaweng,Thailand,2,0,0,0,0,0,0,2
+Le Pes Villas,Khanom,8.8,9.2,Triple Room with Terrace,"2 beds (1 twin, 1 queen)",155.75300000000001,Khanom,Thailand,1,0,1,0,0,0,0,2
+Hugga Pai,Pai,8.6,8.9,Standard Double Room-High Floor,1 full bed,43.096000000000004,Mae Hong Son,Thailand,0,1,0,0,0,0,0,1
+Top Hostel (Top Mansion),Udon Thani,7.8,8.0,Deluxe Double Room,1 queen bed,43.669000000000004,Udon Thani,Thailand,0,0,1,0,0,0,0,1
+Bangkok Marriott Hotel The Surawongse,"Bang Rak, Bangkok",9.2,9.5,Two-Bedroom Residential Suite,"3 beds (2 twins, 1 king)",976.713,Bangkok,Thailand,2,0,0,1,0,0,0,3
+Bualuang HOTEL,Ang Thong,5.0,0.0,Superior Double Room,1 queen bed,28.466,Ang Thong,Thailand,0,0,1,0,0,0,0,1
+The Pier,Thong Sala,6.6,0.0,Bungalow with Fan,1 king bed,60.582,Thong Sala,Thailand,0,0,0,1,0,0,0,1
+Chatrium Residence Sathon Bangkok,"Sathorn, Bangkok",8.7,9.0,Deluxe One-Bedroom King,1 king bed,215.773,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Le Divine Comedie Beach Resort,Ban Tai,8.5,8.6,Bungalow (2 Adults),1 queen bed,140.943,Ko Phangan,Thailand,0,0,1,0,0,0,0,1
+Surin Majestic Hotel,Surin,8.1,8.4,Deluxe King Room,1 king bed,77.673,Surin,Thailand,0,0,0,1,0,0,0,1
+Surin Majestic Hotel,Surin,8.1,8.4,Deluxe King Room,1 king bed,77.673,Surin,Thailand,0,0,0,1,0,0,0,1
+Rajthani Hotel - SHA Certified,Suratthani,8.1,8.3,Standard Double Room,1 queen bed,38.056,Suratthani,Thailand,0,0,1,0,0,0,0,1
+Phu Pha Nam Resort,Dan Sai,7.0,0.0,Standard Double or Twin Room,1 full bed,77.634,Dan Sai,Thailand,0,1,0,0,0,0,0,1
+Oasis Koh Chang,"Lonely Beach, Ko Chang",8.7,8.0,Bungalow with Fan,1 full bed,47.135,Ko Chang,Thailand,0,1,0,0,0,0,0,1
+De Princess Hotel Udonthani,Udon Thani,9.0,9.4,Superior Double Room,1 king bed,124.039,Udon Thani,Thailand,0,0,0,1,0,0,0,1
+Nantrungjai Boutique Hotel,Nan,8.7,8.9,Boutique Room,Multiple bed types,93.044,Nan,Thailand,0,0,0,0,0,0,0,0
+"Vana Belle, A Luxury Collection Resort, Koh Samui","Chaweng Noi Beachfront, Chaweng Noi Beach",9.3,9.4,"Premium Ocean Pool, 1 Bedroom Suite, 1 King, Sofa bed","2 beds (1 king, 1 sofa bed)",1382.913,Chaweng Noi Beach,Thailand,0,0,0,1,0,0,0,2
+Grande Centre Point Hotel Terminal21,"Wattana, Bangkok",8.6,9.0,Grand Suite Double Room,1 queen bed,630.2280000000001,Bangkok,Thailand,0,0,1,0,0,0,0,1
+The Passage Samui Pool Villas with Private Beach,Ban Bang Po,7.9,8.4,Villa with Garden View,Multiple bed types,237.997,Ban Bang Po,Thailand,0,0,0,0,0,0,0,0
+Sriwilai Sukhothai,"Mueang Kao, Sukhothai",8.7,9.1,Superior Double Room,1 full bed,189.233,Sukhothai,Thailand,0,1,0,0,0,0,0,1
+See Through Resort Haad Yao,Haad Yao,7.9,0.0,Deluxe Double or Twin Room with Pool View,   1 double or 2 twins,207.8,Ko Phangan,Thailand,2,0,0,0,0,0,0,2
+Thosawan Resort  ,Khong Chiam,8.4,8.6,Deluxe Room,1 king bed,47.227000000000004,Khong Chiam,Thailand,0,0,0,1,0,0,0,1
+Thosawan Resort  ,Khong Chiam,8.4,8.6,Deluxe Room,1 king bed,47.227000000000004,Khong Chiam,Thailand,0,0,0,1,0,0,0,1
+Siam Bay Resort,"Kai Bae Beach, Ko Chang",8.2,8.1,Villa with Sea View,1 queen bed,217.999,Ko Chang,Thailand,0,0,1,0,0,0,0,1
+Dheva Mantra Resort - SHA,Kanchanaburi City,7.9,8.2,Deluxe Double or Twin Room with River View,   1 double or 2 twins,250.36700000000002,Kanchanaburi City,Thailand,2,0,0,0,0,0,0,2
+Alina Grande Hotel & Resort,"White Sand Beach, Ko Chang",8.0,8.1,Deluxe Queen Room with Two Queen Beds,2 full beds,55.896,Ko Chang,Thailand,0,2,0,0,0,0,0,2
+U Nimman Chiang Mai,"Nimmanhaemin, Chiang Mai",8.9,9.3,Premium Deluxe (24 Hours Use of Room),Multiple bed types,253.806,Chiang Mai,Thailand,0,0,0,0,0,0,0,0
+Natee The Riverfront Hotel Kanchanaburi,Kanchanaburi City,8.4,8.8,Deluxe Double or Twin Room with River View,1 full bed,367.375,Kanchanaburi City,Thailand,0,1,0,0,0,0,0,1
+The View Chiang Dao Hotel,Chiang Dao,9.8,10.0,Standard King Room,1 king bed,45.998,Chiang Dao,Thailand,0,0,0,1,0,0,0,1
+Moon River Resort Phimai,Phimai,8.2,8.2,Bungalow - Water Front,1 queen bed,44.64,Phimai,Thailand,0,0,1,0,0,0,0,1
+Lanta Fa Rung Beach Resort,"Klong Khong Beach, Ko Lanta",9.2,9.2,Bungalow with Fan,1 king bed,38.17,Ko Lanta,Thailand,0,0,0,1,0,0,0,1
+dusitD2 Hua Hin - SHA Extra Plus,Hua Hin,8.8,9.3,Deluxe Hillside King Room,1 king bed,169.61,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+Sabai Sabai Sukhothai - SHA PLUS,Ban Khlong Krachong,8.2,8.7,Superior Double or Twin Room,1 full bed,122.92,Ban Khlong Krachong,Thailand,0,1,0,0,0,0,0,1
+Rustic River Boutique,"Chang Moi, Chiang Mai",8.7,9.1,Superior Twin Room,2 twin beds,50.801,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Impress Resort,Thalang,8.4,8.8,Deluxe Double or Twin Room,"3 beds (2 twins, 1 queen)",106.839,Thalang,Thailand,2,0,1,0,0,0,0,3
+At Pingnakorn Hotel,"Nimmanhaemin, Chiang Mai",8.9,9.3,Deluxe Double Room,1 king bed,131.24,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+The Village Coconut Island Beach Resort - SHA Extra Plus,Phuket,8.0,8.4,Deluxe Twin Suite,2 twin beds,343.789,Phuket,Thailand,2,0,0,0,0,0,0,2
+Arden Hotel and Residence by At Mind,Pattaya,8.6,8.9,Deluxe Double or Twin Room with Pool View,   1 double or 2 twins,195.304,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Riva Surya Bangkok - SHA Extra Plus,"Khaosan, Bangkok",8.9,9.4,Deluxe Double or Twin Room with River View and Balcony,   1 double or 2 twins,440.373,Bangkok,Thailand,2,0,0,0,0,0,0,2
+The MACEO Hotel,Nong Khai,8.4,8.8,Standard Double Room,1 queen bed,187.615,Nong Khai,Thailand,0,0,1,0,0,0,0,1
+Eastin Grand Hotel Sathorn,"Sathorn, Bangkok",9.2,9.6,Superior Sky Double or Twin Room,Multiple bed types,323.793,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Pruksa Garden Hotel,Phu Wiang,8.4,8.5,VIP Double Room,1 full bed,51.756,Phu Wiang,Thailand,0,1,0,0,0,0,0,1
+Thongtakian Resort,"Thong Takian, Lamai",9.0,9.0,Superior Double Room,1 full bed,112.459,Lamai,Thailand,0,1,0,0,0,0,0,1
+The Original Orange Hotel,Nakhon Si Thammarat,8.4,8.6,Deluxe Double or Twin Room,   1 double or 2 twins,55.052,Nakhon Si Thammarat,Thailand,2,0,0,0,0,0,0,2
+Khaolak Merlin Resort - SHA Extra Plus,"Khao Lak Beach, Khao Lak",8.9,9.3,Deluxe Double or Twin Room,1 king bed,287.17,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+The Hotspring Beach Resort & Spa - SHA Extra Plus,Natai Beach,7.9,8.1,Deluxe Twin Room,2 twin beds,181.14600000000002,Natai Beach,Thailand,2,0,0,0,0,0,0,2
+BCP Hotel,Ban Chang,7.4,8.1,Superior Double Room,1 full bed,58.161,Ban Chang,Thailand,0,1,0,0,0,0,0,1
+Pakasai Resort - SHA Extra plus,Ao Nang Beach,8.3,8.4,Deluxe Double or Twin Room,   1 double or 2 twins,177.911,Krabi,Thailand,2,0,0,0,0,0,0,2
+Baan Haad Ngam Boutique Resort - SHA Extra Plus,"Chaweng Beachfront, Chaweng",8.2,8.4,Superior Garden Room with One-way Airport Transfer,"3 beds (2 twins, 1 queen)",307.024,Chaweng,Thailand,2,0,1,0,0,0,0,3
+Phumektawan  Hotel&Restaurant,Mae Salong,8.4,8.6,King Suite,1 king bed,116.45100000000001,Mae Salong,Thailand,0,0,0,1,0,0,0,1
+Amari Hua Hin,"Khao Takiab, Hua Hin",8.5,9.0,Club One Bedroom Suite Pool View,1 king bed,671.234,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+"Sheraton Grande Sukhumvit, a Luxury Collection Hotel, Bangkok","Khlong Toei, Bangkok",9.2,9.6,Luxury King Room,1 king bed,650.702,Bangkok,Thailand,0,0,0,1,0,0,0,1
+  Pillow Inn Hotel,Ban Khao Hin Son,8.7,8.3,Standard Double Room,1 king bed,48.521,Ban Khao Hin Son,Thailand,0,0,0,1,0,0,0,1
+Nakakiri Resort & Spa,Hin Dat,6.7,0.0,Floating Room,   1 double or 2 twins,67.07600000000001,Hin Dat,Thailand,2,0,0,0,0,0,0,2
+Zing Resort & Spa,"Dongtan Beach, Pattaya South",8.2,8.6,Pool Suite,1 queen bed,155.703,Pattaya South,Thailand,0,0,1,0,0,0,0,1
+Anantara Hua Hin Resort - SHA Certified,Hua Hin,8.8,9.2,Junior Lagoon View Suite,1 king bed,475.61,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+Avarin Resort,Pak Chong,8.7,9.0,Standard King Room,1 king bed,79.516,Pak Chong,Thailand,0,0,0,1,0,0,0,1
+Bluerama - Adults Only,Haad Pleayleam,7.3,0.0,Double Room with Balcony and Sea View,1 queen bed,301.14,Haad Pleayleam,Thailand,0,0,1,0,0,0,0,1
+ibis Styles Chiang Mai,"Chang Phueak, Chiang Mai",8.2,8.4,Standard Double Room,1 queen bed,58.817,Chiang Mai,Thailand,0,0,1,0,0,0,0,1
+Kokotel Pattaya South Beach,Pattaya,7.9,8.3,Superior Twin Room,2 twin beds,71.688,Pattaya,Thailand,2,0,0,0,0,0,0,2
+"VIE Hotel Bangkok, MGallery","Pratunam, Bangkok",8.7,9.1,Deluxe Twin Room,2 twin beds,274.492,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Khaosok Bamboo Huts Resort,Khao Sok National Park,8.5,8.5,Standard Double Room with Fan,1 full bed,58.225,Surat Thani,Thailand,0,1,0,0,0,0,0,1
+Radisson Suites Bangkok Sukhumvit - SHA Extra Plus,"Wattana, Bangkok",7.9,8.4,Deluxe Room,1 king bed,189.93,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Chanathinat Place,Udon Thani,7.5,0.0,Double Room with Balcony,1 full bed,29.113,Udon Thani,Thailand,0,1,0,0,0,0,0,1
+iSanook Resort & Suites Hua Hin - SHA Plus Certified,"Khao Takiab, Hua Hin",8.7,9.3,Suite Pool View,Multiple bed types,239.279,Hua Hin,Thailand,0,0,0,0,0,0,0,0
+Phuket Orchid Resort and Spa - SHA Extra Plus,Karon Beach,7.8,0.0,Deluxe Room,   1 double or 2 twins,261.287,Phuket,Thailand,2,0,0,0,0,0,0,2
+Cross Vibe Bangkok Sukhumvit - formerly X2 Vibe Bangkok Sukhumvit - SHA Plus,"Khlong Toei, Bangkok",8.2,8.7,One-Bedroom Suite,1 full bed,173.663,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Parichat Ban Dung Hotel,Ban Dung,7.8,0.0,Double Room,1 king bed,48.521,Ban Dung,Thailand,0,0,0,1,0,0,0,1
+Solitaire Bangkok Sukhumvit 11,"Wattana, Bangkok",8.0,8.3,Grand Deluxe Double,1 queen bed,260.29,Bangkok,Thailand,0,0,1,0,0,0,0,1
+Ramada Resort by Wyndham Khao Lak - SHA Plus Extra,"Bang Niang Beach, Khao Lak",9.0,9.3,Ocean Front Villa,1 king bed,634.589,Phang Nga,Thailand,0,0,0,1,0,0,0,1
+ ,Ban Muang Baeng,8.5,8.8,Economy Quadruple Room with Shared Bathroom,"2 beds (1 bunk bed, 1 queen)",88.17,Ban Muang Baeng,Thailand,0,0,1,0,0,0,0,2
+Destination Resorts Phuket Surin Beach - SHA Extra Plus,Surin Beach,6.7,0.0,Superior King Room,1 king bed,149.05700000000002,Phuket,Thailand,0,0,0,1,0,0,0,1
+The One Residence,Udon Thani,8.0,8.2,Superior King Room,1 queen bed,34.935,Udon Thani,Thailand,0,0,1,0,0,0,0,1
+Avani+ Hua Hin Resort,Cha Am,8.5,8.7,AVANI Pool Villa with Club Lounge Access,1 king bed,803.612,Cha Am,Thailand,0,0,0,1,0,0,0,1
+Baanfai Guesthouse Chiangkhong,Chiang Khong,8.0,8.2,Standard Double Room with Air Conditioning,1 full bed,32.347,Chiang Rai,Thailand,0,1,0,0,0,0,0,1
+Le Vernissage @ Goad Avadhess Hospitality,Pattaya,7.2,0.0,Budget Double or Twin Room,2 twin beds,57.74,Pattaya,Thailand,2,0,0,0,0,0,0,2
+Aukotan Place,"Chalok Baan Kao, Ko Tao",8.6,8.9,Superior Double Room,1 full bed,66.543,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+Avani Ao Nang Cliff Krabi Resort - SHA Extra Plus,Ao Nang Beach,8.3,8.7,AVANI Room,   1 double or 2 twins,174.864,Krabi,Thailand,2,0,0,0,0,0,0,2
+Selina Serenity Rawai Phuket,Rawai Beach,8.0,8.6,Cabana,1 full bed,168.945,Phuket,Thailand,0,1,0,0,0,0,0,1
+Sareeraya Villas & Suites - SHA Extra Plus,"Chaweng Beachfront, Chaweng",8.6,9.1,Luxury Suite,1 king bed,417.704,Chaweng,Thailand,0,0,0,1,0,0,0,1
+100 Islands Resort & Spa,Suratthani,7.9,8.2,Superior Double or Twin Room,Multiple bed types,58.225,Suratthani,Thailand,0,0,0,0,0,0,0,0
+Furama Chiang Mai,"Chang Phueak, Chiang Mai",7.6,0.0,Superior Double or Twin Room,Multiple bed types,88.971,Chiang Mai,Thailand,0,0,0,0,0,0,0,0
+Melia Chiang Mai,"Chang Moi, Chiang Mai",9.0,9.4,Meliá Room,   1 double or 2 twins,204.19400000000002,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Pinnacle Samui Resort SHA Plus,Mae Nam,8.1,8.3,Hotel Room,1 queen bed,120.132,Ko Samui,Thailand,0,0,1,0,0,0,0,1
+Veranda Resort & Villas Hua Hin Cha Am,Cha Am,8.3,8.7,Pool King Suite with Private Pool,1 king bed,393.725,Cha Am,Thailand,0,0,0,1,0,0,0,1
+Pullman Bangkok King Power,"Pratunam, Bangkok",8.8,9.2,Executive Double Room with Executive Lounge Access,1 king bed,453.858,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Stay Inn Nathon Samui,Nathon,8.1,8.5,Deluxe Double Room with Side Sea View,1 full bed,64.048,Nathon,Thailand,0,1,0,0,0,0,0,1
+Riverhouse Resort,Mae Sariang,7.9,8.0,Double Room,1 queen bed,97.042,Mae Sariang,Thailand,0,0,1,0,0,0,0,1
+Park Plaza Sukhumvit Bangkok,"Khlong Toei, Bangkok",8.0,8.6,Superior Double or Twin Room,   1 double or 2 twins,170.217,Bangkok,Thailand,2,0,0,0,0,0,0,2
+ RuenRomMai Resort,Ban Klang Mun,9.0,8.3,One-Bedroom House,1 full bed,35.744,Ban Klang Mun,Thailand,0,1,0,0,0,0,0,1
+Sinkiat Thani Hotel,Satun,7.2,0.0,Standard Double or Twin Room,   1 double or 2 twins,51.109,Satun,Thailand,2,0,0,0,0,0,0,2
+Little Paradise Haad Rin Koh Phangan,"Haad Rin Nok, Haad Rin",8.3,8.4,Twin Room with Balcony,2 twin beds,166.25900000000001,Haad Rin,Thailand,2,0,0,0,0,0,0,2
+Livotel Hotel Lat Phrao Bangkok,Bangkok,7.1,0.0,Superior Double Room,1 king bed,42.039,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Nice Beach Resort Koh Pha-ngan,Thong Nai Pan Yai,8.6,8.5,Family Room (4 Adults),2 queen beds,110.628,Not specified,Thailand,0,0,2,0,0,0,0,2
+Chedi Home -SHA Extra Plus,"Si Phum, Chiang Mai",8.7,8.9,Standard Double Room,1 king bed,67.03,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Home Phang-Nga Guesthouse,Phangnga,9.1,9.2,Standard Double Room,1 king bed,58.225,Phangnga,Thailand,0,0,0,1,0,0,0,1
+Home Phang-Nga Guesthouse,Phangnga,9.1,9.2,Standard Double Room,1 king bed,58.225,Phangnga,Thailand,0,0,0,1,0,0,0,1
+Suankaew Art Cottage,Ban Tha Sai,8.0,0.0,Studio with Balcony,1 king bed,87.4,Ban Tha Sai,Thailand,0,0,0,1,0,0,0,1
+Melati Beach Resort & Spa - SHA Extra Plus Certified,Choeng Mon Beach,8.3,8.9,Grand Deluxe Double or Twin Room,Multiple bed types,418.97700000000003,Choeng Mon Beach,Thailand,0,0,0,0,0,0,0,0
+B Space Residence,Nong Prue,8.1,8.3,Standard Double Room,1 queen bed,43.611000000000004,Nong Prue,Thailand,0,0,1,0,0,0,0,1
+B Space Residence,Nong Prue,8.1,8.3,Standard Double Room,1 queen bed,43.611000000000004,Nong Prue,Thailand,0,0,1,0,0,0,0,1
+   Sohground Boutique Resort,Prakhon Chai,7.2,0.0,Standard King Room,1 king bed,32.347,Prakhon Chai,Thailand,0,0,0,1,0,0,0,1
+   Sohground Boutique Resort,Prakhon Chai,7.2,0.0,Standard King Room,1 king bed,32.347,Prakhon Chai,Thailand,0,0,0,1,0,0,0,1
+La Ong Lay,"Aow Yai Beach, Ko Phayam",7.9,0.0,Wooden Bungalow with Fan Partial Seaview,1 king bed,161.737,Ko Phayam,Thailand,0,0,0,1,0,0,0,1
+Terre Terrace Glamping - Doichang,Ban Huai Khai,8.0,8.0,Bungalow,1 twin bed,42.052,Ban Huai Khai,Thailand,1,0,0,0,0,0,0,1
+2 Feel Bed Station,Udon Thani,8.4,8.7,Modern Double Room,1 queen bed,51.109,Udon Thani,Thailand,0,0,1,0,0,0,0,1
+Travelodge Nimman,Chiang Mai,9.3,9.5,Superior,Multiple bed types,92.84400000000001,Chiang Mai,Thailand,0,0,0,0,0,0,0,0
+Tea Tree Boutique Resort,Rawai Beach,8.9,9.1,Deluxe Double Room with Balcony,1 king bed,97.042,Phuket,Thailand,0,0,0,1,0,0,0,1
+At Samui Haus,Lipa Noi,8.2,8.4,Superior Garden Room,1 queen bed,118.269,Ko Samui,Thailand,0,0,1,0,0,0,0,1
+Baan Duangkaew Resort SHA Extra Plus,"Khao Takiab, Hua Hin",8.7,9.2,Deluxe Room,1 king bed,199.852,Hua Hin,Thailand,0,0,0,1,0,0,0,1
+BelAire Bangkok Sukhumvit - SHA Extra Plus,"Wattana, Bangkok",7.9,8.1,Special Offer – Deluxe Double or Twin,Multiple bed types,150.234,Bangkok,Thailand,0,0,0,0,0,0,0,0
+BB Mantra Boutique Hotel,"Si Phum, Chiang Mai",8.6,8.8,Superior room with Street View (No balcony),Multiple bed types,90.83200000000001,Chiang Mai,Thailand,0,0,0,0,0,0,0,0
+The Moon Night Hotel,"Khaosan, Bangkok",7.9,8.0,Family Room,2 twin beds,110.075,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Areeya Resort,Watthana Nakhon,8.9,9.2,Superior Bungalow,1 full bed,40.758,Watthana Nakhon,Thailand,0,1,0,0,0,0,0,1
+Baanfah Resort Samui,Mae Nam,8.5,8.9,Superior Double or Twin Room with Garden View,   1 double or 2 twins,124.538,Ko Samui,Thailand,2,0,0,0,0,0,0,2
+"The Briza Beach Resort, Samui - SHA Plus","Chaweng Beachfront, Chaweng",7.2,0.0,Deluxe Double Room with Balcony,1 king bed,177.92000000000002,Chaweng,Thailand,0,0,0,1,0,0,0,1
+The Moonlight Resort,Ban Chang,8.3,8.8,Economy Double Room,1 full bed,37.847,Ban Chang,Thailand,0,1,0,0,0,0,0,1
+Maraya Hotel & Resort -SHA Plus,"Chiang Mai Riverside, Chiang Mai",8.5,8.9,Pool Deluxe,   1 double or 2 twins,140.213,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Tungtong Beach Villas,Ban Khao Khwang (2),8.5,9.3,Deluxe Queen Room with Two Queen Beds - Garden View,2 queen beds,322.551,Ban Khao Khwang,Thailand,0,0,2,0,0,0,0,2
+Anmol Hotel Thai Smile Group,Pattaya,8.1,8.2,Deluxe Double or Twin Room with Balcony,   1 double or 2 twins,37.032000000000004,Pattaya,Thailand,2,0,0,0,0,0,0,2
+The Neuf@Ratchada,"Ratchadaphisek, Ban Na Song",8.8,9.2,King Room,1 king bed,74.677,Ban Na Song,Thailand,0,0,0,1,0,0,0,1
+The Contrast i Hotel,Pluak Daeng,7.6,0.0,Standard Twin Room,2 twin beds,145.564,Pluak Daeng,Thailand,2,0,0,0,0,0,0,2
+Pongsakorn Boutique Resort -SHA Extra Plus,Lat Krabang,7.6,0.0,Superior King Room,1 king bed,57.514,Lat Krabang,Thailand,0,0,0,1,0,0,0,1
+Pongsakorn Boutique Resort -SHA Extra Plus,Lat Krabang,7.6,0.0,Superior King Room,1 king bed,57.514,Lat Krabang,Thailand,0,0,0,1,0,0,0,1
+Pullman Khao Lak Resort,Khao Lak,8.2,8.9,Deluxe King or Twin Room with Pool View,Multiple bed types,118.12700000000001,Phang Nga,Thailand,0,0,0,0,0,0,0,0
+The Mangrove by Blu Monkey Phuket - SHA Extra Plus,Panwa Beach,8.3,8.9,"Studio Villa with Garden View Free Kayak, Paddle Board and Playground",   1 double or 2 twins,216.728,Phuket,Thailand,2,0,0,0,0,0,0,2
+Mee Place,Amphoe Kumphawapi,8.3,8.3,Standard Double Room,1 king bed,38.17,Amphoe Kumphawapi,Thailand,0,0,0,1,0,0,0,1
+Coral View Resort,Ko Tao,8.5,8.7,Hillside Cabin with Air Conditioning,1 full bed,129.698,Ko Tao,Thailand,0,1,0,0,0,0,0,1
+POR Santitham,"Si Phum, Chiang Mai",8.4,8.6,Double Room,1 king bed,87.985,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Beyond Resort Khaolak - SHA Plus,"Bangsak Beach, Khao Lak",8.4,8.6,Villa Garden,1 king bed,302.579,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+Beyond Resort Khaolak - SHA Plus,"Bangsak Beach, Khao Lak",8.4,8.6,Villa Garden,1 king bed,302.579,Khao Lak,Thailand,0,0,0,1,0,0,0,1
+Had Lamae Resort,Ban Pak Nam Lamae (1),6.8,0.0,Standard Double or Twin Room,1 twin bed,58.225,Ban Pak Nam Lamae,Thailand,1,0,0,0,0,0,0,1
+Banbua Grand Udon,Udon Thani,8.8,9.2,Superior Queen Room,1 full bed,85.043,Udon Thani,Thailand,0,1,0,0,0,0,0,1
+,Ban Nong Ko,0.0,0.0,Deluxe King Room,1 king bed,45.933,Ban Nong Ko,Thailand,0,0,0,1,0,0,0,1
+Paradox Resort Phuket - SHA Plus,Karon Beach,8.2,8.8,Superior Twin Room,2 twin beds,252.501,Phuket,Thailand,2,0,0,0,0,0,0,2
+   ,Tha Kradan,7.6,8.2,Queen Suite with Spa Bath,1 queen bed,340.111,Tha Kradan,Thailand,0,0,1,0,0,0,0,1
+Citrus Sukhumvit 13 Nana Bangkok by Compass Hospitality,"Wattana, Bangkok",7.3,0.0,Superior Room,Multiple bed types,92.68,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Home of River Kwai,Kanchanaburi City,9.5,9.6,Standard Twin Room with Shared Bathroom,4 twin beds,27.754,Kanchanaburi City,Thailand,4,0,0,0,0,0,0,4
+Baan Nilrath Hotel - SHA Extra Plus,Hua Hin,8.6,8.9,Superior Double Room,1 queen bed,103.467,Hua Hin,Thailand,0,0,1,0,0,0,0,1
+Kokotel Krabi Oasis,Ao Nang Beach,8.4,9.0,Deluxe Twin Room,2 twin beds,57.116,Krabi,Thailand,2,0,0,0,0,0,0,2
+Nap In Fest,"Fa Ham, Chiang Mai",8.6,8.9,Deluxe Double Room,1 full bed,71.763,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+Baan Laimai Beach Resort & Spa - SHA Extra Plus,Patong Beach,8.1,8.5,Superior Double or Twin Room - No View,   1 double or 2 twins,362.939,Phuket,Thailand,2,0,0,0,0,0,0,2
+ibis Chiang Mai Nimman Journeyhub,"Chang Phueak, Chiang Mai",8.8,9.3,Standard Double Room,1 full bed,65.148,Chiang Mai,Thailand,0,1,0,0,0,0,0,1
+Riverside Palm Resort - Surat Thani,Ban Bang Pho,8.6,8.8,Bungalow,1 king bed,34.935,Ban Bang Pho,Thailand,0,0,0,1,0,0,0,1
+Promma Farm Resort,Ban Tat Ton (1),8.2,8.5,Queen Room with Garden View,1 king bed,42.052,Ban Tat Ton,Thailand,0,0,0,1,0,0,0,1
+Barali Beach Resort & Spa,"Klong Prao Beach, Ko Chang",8.1,8.4,Premier Deluxe Villa,"2 beds (1 sofa bed, 1 queen)",216.428,Ko Chang,Thailand,0,0,1,0,0,0,0,2
+Bangkok Marriott Hotel Sukhumvit,"Wattana, Bangkok",8.6,9.1,Deluxe Room with King Bed,1 king bed,400.673,Bangkok,Thailand,0,0,0,1,0,0,0,1
+Villa The Wave,Amphoe Koksamui,8.8,8.5,Three-Bedroom Villa,"4 beds (2 twins, 2 queens)",673.6610000000001,Amphoe Koksamui,Thailand,2,0,2,0,0,0,0,4
+"Koh Jum Beach Villas ""A member of Secret Retreats""",Ko Jum,9.6,9.9,Deluxe Villa,3 king beds,1348.872,Ko Jum,Thailand,0,0,0,3,0,0,0,3
+SR Residence Hotel,Phetchabun,8.3,8.6,Standard Double or Twin Room,1 queen bed,77.634,Phetchabun,Thailand,0,0,1,0,0,0,0,1
+SR Residence Hotel,Phetchabun,8.3,8.6,Standard Double or Twin Room,1 queen bed,77.634,Phetchabun,Thailand,0,0,1,0,0,0,0,1
+"Paradise Beach Resort, Koh Samui - SHA Extra Plus",Mae Nam,8.3,8.7,Grand Deluxe Double Room,1 king bed,262.015,Ko Samui,Thailand,0,0,0,1,0,0,0,1
+Pavilion Samui Villas and Resort - SHA Extra Plus,Lamai,8.5,8.7,Deluxe Double Room with Balcony,Multiple bed types,294.824,Lamai,Thailand,0,0,0,0,0,0,0,0
+Pavilion Samui Villas and Resort - SHA Extra Plus,Lamai,8.5,8.7,Deluxe Double Room with Balcony,Multiple bed types,294.824,Lamai,Thailand,0,0,0,0,0,0,0,0
+Riverside House Hotel SHA Extra Plus,"Chang Moi, Chiang Mai",8.6,8.7,Deluxe Plus with Balcony,   1 double or 2 twins,81.516,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+YYK Homestay,Ban Khlong Krang,6.8,0.0,Four-Bedroom House,"5 beds (2 twins, 2 fulls, 1 sofa bed)",148.475,Ban Khlong Krang,Thailand,2,2,0,0,0,0,0,5
+Aster Hotel and Residence by At Mind,Pattaya,8.8,9.1,New Deluxe Room,1 queen bed,149.501,Pattaya,Thailand,0,0,1,0,0,0,0,1
+Life Hotel Rong Khun,Ban Mai,8.4,8.4,Queen Room,1 queen bed,49.104,Ban Mai,Thailand,0,0,1,0,0,0,0,1
+The Areaac,Ban Chomphu,8.2,8.1,Standard Double Room,1 full bed,42.052,Ban Chomphu,Thailand,0,1,0,0,0,0,0,1
+Phuphet Hill Resort,Phu Ruea,7.6,0.0,Double or Twin Room with View,"2 beds (1 twin, 1 queen)",45.286,Phu Ruea,Thailand,1,0,1,0,0,0,0,2
+OYO 75397 Is Am Are Stay Boutique,Ratchaburi,8.9,9.1,Deluxe King Room,1 king bed,51.49,Ratchaburi,Thailand,0,0,0,1,0,0,0,1
+OYO 75397 Is Am Are Stay Boutique,Ratchaburi,8.9,9.1,Deluxe King Room,1 king bed,51.49,Ratchaburi,Thailand,0,0,0,1,0,0,0,1
+Mut Mee Garden Guest House,Nong Khai,8.3,8.3,Double or Twin Room with Shared Bathroom,   1 double or 2 twins,25.231,Nong Khai,Thailand,2,0,0,0,0,0,0,2
+Mercure Chiang Mai,"Si Phum, Chiang Mai",7.9,8.4,Standard King Room,1 king bed,72.366,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+Lotus Pang Suan Kaew Hotel,Chiang Mai,7.3,0.0,Superior Twin Room,2 twin beds,72.58800000000001,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Ailay,"Aow Yai Beach, Ko Phayam",8.7,8.7,Deluxe Double Room with Partial Sea View,1 king bed,226.43200000000002,Ko Phayam,Thailand,0,0,0,1,0,0,0,1
+Bor Saen Pool Villa,Bor Saen,8.5,8.5,One-Bedroom Villa with Private Pool,1 queen bed,280.45300000000003,Bor Saen,Thailand,0,0,1,0,0,0,0,1
+Apsara Beachfront Resort & Villa - SHA Extra Plus,"Laem Pakarang Beach, Khao Lak",8.7,9.0,Superior Double or Twin Room,   1 double or 2 twins,260.259,Khao Lak,Thailand,2,0,0,0,0,0,0,2
+The Elysium Pra?umnak Pattaya - By SHG,Pattaya South,9.1,9.5,Executive Suite,1 king bed,226.45600000000002,Pattaya,Thailand,0,0,0,1,0,0,0,1
+Le Resort and Villas,Rawai Beach,8.9,9.3,Deluxe Twin Room,2 twin beds,132.463,Phuket,Thailand,2,0,0,0,0,0,0,2
+Chi Art Series Hotel Bangkok,"Khlong Toei, Bangkok",9.2,9.7,Superior Double or Twin Room with City View,   1 double or 2 twins,207.496,Bangkok,Thailand,2,0,0,0,0,0,0,2
+Khao Sok Tree House Resort,Khao Sok National Park,8.5,8.3,Standard Bungalow,1 full bed,116.45100000000001,Surat Thani,Thailand,0,1,0,0,0,0,0,1
+Cinnamon Beach Villas,"Thong Takian, Lamai",8.0,8.4,Bungalow with Sea View,1 full bed,103.512,Lamai,Thailand,0,1,0,0,0,0,0,1
+DOO KHAO,Ban Chong Tako,7.5,8.8,Standard Queen Room,1 twin bed,41.219,Ban Chong Tako,Thailand,1,0,0,0,0,0,0,1
+Chala Number6,"Si Phum, Chiang Mai",8.9,9.5,Deluxe Twin Room,2 twin beds,426.99,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Hyatt Place Bangkok Sukhumvit - SHA Extra Plus Certified,"Khlong Toei, Bangkok",8.4,9.0,Standard King no Sofa Bed,1 king bed,228.309,Bangkok,Thailand,0,0,0,1,0,0,0,1
+The Lamai Samui - formerly Le Méridien Koh Samui Resort & Spa - SHA Extra Plus,"Thong Takian, Lamai",8.9,9.4,Deluxe Suite - The Veranda,1 full bed,501.959,Lamai,Thailand,0,1,0,0,0,0,0,1
+Touch the Wind,Chaiyaphum,9.3,9.2,Deluxe Double Room with Balcony,1 queen bed,56.192,Chaiyaphum,Thailand,0,0,1,0,0,0,0,1
+Moose Hotel Chiangmai,"Wat Ket, Chiang Mai",8.8,9.2,Deluxe Double Room,1 king bed,139.741,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+G Residence,Pattaya South,8.7,8.8,Studio with Balcony,1 king bed,72.274,Pattaya,Thailand,0,0,0,1,0,0,0,1
+,Sichon,9.1,9.2,Standard Double Room,1 king bed,72.2,Sichon,Thailand,0,0,0,1,0,0,0,1
+Hyde Park Chiangmai,Chiang Mai,8.8,9.2,Deluxe Park View,   1 double or 2 twins,108.44500000000001,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Sandalwood Luxury Villa Resort - SHA Extra Plus,Lamai,8.9,9.2,One-Bedroom Suite with Spa Bath,1 king bed,241.497,Lamai,Thailand,0,0,0,1,0,0,0,1
+Sandalwood Luxury Villa Resort - SHA Extra Plus,Lamai,8.9,9.2,One-Bedroom Suite with Spa Bath,1 king bed,241.497,Lamai,Thailand,0,0,0,1,0,0,0,1
+Thai House Beach Resort,Lamai,8.0,8.1,Standard Classic Double or Twin Room,   1 double or 2 twins,213.309,Lamai,Thailand,2,0,0,0,0,0,0,2
+Thai House Beach Resort,Lamai,8.0,8.1,Standard Classic Double or Twin Room,   1 double or 2 twins,213.309,Lamai,Thailand,2,0,0,0,0,0,0,2
+Koh Jum Delight Beach,Ko Jum,8.5,8.6,Standard Double Room with Fan,1 full bed,116.45100000000001,Ko Jum,Thailand,0,1,0,0,0,0,0,1
+Theatre Residence,"Riverside, Bangkok",8.7,9.2,Deluxe Room,Multiple bed types,197.507,Bangkok,Thailand,0,0,0,0,0,0,0,0
+Marvin Suites Hotel,"Sathorn, Bangkok",8.3,8.6,Deluxe Studio,1 full bed,57.917,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Chada Mantra Hotel,"Si Phum, Chiang Mai",8.9,9.1,Superior Double or Twin Room,1 king bed,98.19800000000001,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+The Chill House,Ban Wang Khanai,7.7,8.4,Deluxe Room (2 Adults + 1 Child),1 king bed,39.593,Ban Wang Khanai,Thailand,0,0,0,1,0,0,0,1
+Siamgrand Hotel,Udon Thani,8.2,8.7,Superior King Room,1 king bed,71.164,Udon Thani,Thailand,0,0,0,1,0,0,0,1
+Centara Grand at Central Plaza Ladprao Bangkok,"Chatuchak, Bangkok",8.6,8.9,Deluxe Twin Room,2 twin beds,222.398,Bangkok,Thailand,2,0,0,0,0,0,0,2
+De Lit,Ubon Ratchathani,8.4,8.2,Standard King Room,1 king bed,80.337,Ubon Ratchathani,Thailand,0,0,0,1,0,0,0,1
+Mariya Boutique Hotel At Suvarnabhumi Airport,Lat Krabang,8.0,8.2,Superior Queen Room-Room only-No Airport Transfer,1 queen bed,75.111,Lat Krabang,Thailand,0,0,1,0,0,0,0,1
+Mariya Boutique Hotel At Suvarnabhumi Airport,Lat Krabang,8.0,8.2,Superior Queen Room-Room only-No Airport Transfer,1 queen bed,75.111,Lat Krabang,Thailand,0,0,1,0,0,0,0,1
+Gold Airport Suites,Lat Krabang,7.1,0.0,Standard Double Room,1 queen bed,61.342,Lat Krabang,Thailand,0,0,1,0,0,0,0,1
+Gold Airport Suites,Lat Krabang,7.1,0.0,Standard Double Room,1 queen bed,61.342,Lat Krabang,Thailand,0,0,1,0,0,0,0,1
+River Tree Resort,Amphoe Chiang Khan,8.6,8.6,Junior Suite with Balcony,1 king bed,248.78,Amphoe Chiang Khan,Thailand,0,0,0,1,0,0,0,1
+Maesalong Villa,Mae Salong,7.5,0.0,Standard Double Room with Fan,1 full bed,51.756,Mae Salong,Thailand,0,1,0,0,0,0,0,1
+The Chic Lipe,Ko Lipe,8.8,8.9,Double Bed in Mixed Dormitory Room,1 full bed,80.869,Ko Lipe,Thailand,0,1,0,0,0,0,0,1
+Marrakesh Hua Hin Resort & Spa,Hua Hin,8.4,8.7,Fountain Pool Suite,1 queen bed,376.847,Hua Hin,Thailand,0,0,1,0,0,0,0,1
+Top North Hotel,"Si Phum, Chiang Mai",7.2,0.0,Standard Double or Twin Room,   1 double or 2 twins,53.373000000000005,Chiang Mai,Thailand,2,0,0,0,0,0,0,2
+Villa De Khaosan by Chillax - SHA Extra Plus,"Khaosan, Bangkok",8.2,8.7,Classic Double Room Street View,1 full bed,135.374,Bangkok,Thailand,0,1,0,0,0,0,0,1
+Bestiny Hotel & Restaurant Phetchabun,Ban Na Chaliang,7.7,8.1,Standard Double Room,1 king bed,53.697,Ban Na Chaliang,Thailand,0,0,0,1,0,0,0,1
+Ban Ao Thong,Trang,8.4,8.6,Standard Double Room - No Window,1 queen bed,45.286,Trang,Thailand,0,0,1,0,0,0,0,1
+Shama Petchburi 47 Bangkok - Former Amari Residences Bangkok,"Huai Khwang, Bangkok",8.2,8.4,Junior Suite,"2 beds (1 sofa bed, 1 queen)",238.443,Bangkok,Thailand,0,0,1,0,0,0,0,2
+Asa Hotel,"Hai Ya, Chiang Mai",9.2,9.3,Standard Double Room,1 king bed,99.63,Chiang Mai,Thailand,0,0,0,1,0,0,0,1
+NornD@Rayong,Ban Khao Yai Chum,9.7,10.0,Double Room,1 full bed,69.871,Ban Khao Yai Chum,Thailand,0,1,0,0,0,0,0,1
+Pullman Bangkok Grande Sukhumvit,"Wattana, Bangkok",7.2,0.0,Premium Deluxe King Room with City View,1 queen bed,280.345,Bangkok,Thailand,0,0,1,0,0,0,0,1
+House of Sanskara,"Haad Rin Nok, Haad Rin",8.8,8.6,Bungalow with Garden View,1 king bed,167.976,Haad Rin,Thailand,0,0,0,1,0,0,0,1
+Plaza Huahin,"Hua Hin Night Market, Hua Hin",7.6,0.0,Budget Double Room,1 queen bed,35.487,Hua Hin,Thailand,0,0,1,0,0,0,0,1
+Radisson Resort and Suites Phuket,Kamala Beach,6.3,0.0,One-Bedroom Suite with Terrace,,207.671,Kamala Beach,Thailand,0,0,0,0,0,0,0,0
+Radisson Resort and Suites Phuket,Kamala Beach,6.3,0.0,One-Bedroom Suite with Terrace,,207.671,Kamala Beach,Thailand,0,0,0,0,0,0,0,0
+Lamai Coconut Beach Resort,"Lamai City Center, Lamai",8.1,8.4,Superior - Pool View,1 queen bed,135.213,Lamai,Thailand,0,0,1,0,0,0,0,1
+Mida Grande Hotel Dhavaravati Nakhon Pathom - SHA PLUS,Nakhon Pathom,7.9,8.5,Deluxe Double or Twin Room,   1 double or 2 twins,145.331,Nakhon Pathom,Thailand,2,0,0,0,0,0,0,2
+POSH 41,Salaya,6.7,0.0,Twin Room with Shared Bathroom,1 bunk bed,31.701,Salaya,Thailand,0,0,0,0,0,0,0,0
+"Holiday Inn Express Bangkok Sathorn, an IHG Hotel - SHA Extra Plus Certified","Bang Rak, Bangkok",8.4,8.9,Standard Queen Room,1 queen bed,127.333,Bangkok,Thailand,0,0,1,0,0,0,0,1
+iRabbit Hotel,Prachin Buri,8.6,9.0,Standard King Room,1 king bed,68.577,Prachin Buri,Thailand,0,0,0,1,0,0,0,1
+Apartamentos Montenegro en playa Maspalomas,San Bartolomé,9.3,10.0,Apartment with Sea View,"2 beds (1 full, 1 sofa bed)",167.876,San Bartolomé,Spain,0,1,0,0,0,0,0,2
+TaCH Madrid Airport,"Barajas, Madrid",8.4,8.8,Double or Twin Room with parking included,   1 double or 2 twins,382.342,Madrid,Spain,2,0,0,0,0,0,0,2
+Can Digus,Fornells,8.7,8.6,One-Bedroom Apartment,2 twin beds,165.353,Fornells,Spain,2,0,0,0,0,0,0,2
+Finca Curbelo,Uga,9.3,9.5,Apartment with Terrace,1 full bed,462.988,Uga,Spain,0,1,0,0,0,0,0,1
+Grupotel Monte Feliz,San Agustin,8.0,8.2,Two-Bedroom Apartment (2 Adults),"5 beds (4 twins, 1 sofa bed)",195.917,San Agustin,Spain,4,0,0,0,0,0,0,5
+Rio Sil Carballo,Carballo,8.9,9.3,Double Room with Private Bathroom,1 full bed,170.57500000000002,Carballo,Spain,0,1,0,0,0,0,0,1
+Hostal Aranda,Peniscola,8.5,8.6,Double Room with Balcony - First Floor,1 full bed,224.532,Peniscola,Spain,0,1,0,0,0,0,0,1
+Apartamentos Playazul,Playa de las Americas,7.4,0.0,Two-Bedroom Apartment,"5 beds (4 twins, 1 sofa bed)",299.26300000000003,Playa de las Americas,Spain,4,0,0,0,0,0,0,5
+Hotel Rural y SPA Kinedomus Bienestar,Aranda de Duero,8.5,8.8,Double Room,1 queen bed,299.724,Aranda de Duero,Spain,0,0,1,0,0,0,0,1
+Mimosas Suites Banús,"Puerto Banus, Marbella",8.7,9.1,Two-Bedroom Superior Penthouse with parking and spa,4 twin beds,654.777,Marbella,Spain,4,0,0,0,0,0,0,4
+Ramada Residences by Wyndham Costa del Sol,Mijas Costa,8.2,8.5,Superior Two bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",258.038,Mijas Costa,Spain,2,1,0,0,0,0,0,4
+Hof van Holland Carihuela,"Carihuela, Torremolinos",7.7,0.0,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",273.267,Torremolinos,Spain,2,0,0,0,0,0,0,3
+Apartamentos Wamba,"City Center, Conil de la Frontera",9.6,9.8,One-Bedroom Apartment,1 queen bed,203.715,Conil de la Frontera,Spain,0,0,1,0,0,0,0,1
+Palmanova Beach Apartments by TRH,Palmanova,9.0,9.4,Studio (2 Adults),2 twin beds,283.415,Palmanova,Spain,2,0,0,0,0,0,0,2
+Casa Rural Finca Las Dulces,Chío,9.7,9.4,Apartment - Split Level,2 twin beds,146.207,Chío,Spain,2,0,0,0,0,0,0,2
+Villa Sun and sea 4 front de Mer Playa Rocca Costa teguise,Costa Teguise,8.7,8.8,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",197.379,Costa Teguise,Spain,2,1,0,0,0,0,0,4
+Ancha Village,"Marbella Old Town, Marbella",9.3,9.5,Superior Suite,1 full bed,445.461,Marbella,Spain,0,1,0,0,0,0,0,1
+Castillo,Estepona,7.3,0.0,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",177.526,Estepona,Spain,2,1,0,0,0,0,0,3
+Apartament els Masovers,La Pera,9.4,9.6,One-Bedroom Apartment,1 full bed,252.207,La Pera,Spain,0,1,0,0,0,0,0,1
+Hotel Boutique Molino de Enmedio,Huéneja,9.5,9.7,Family Suite,"2 beds (1 sofa bed, 1 queen)",352.115,Huéneja,Spain,0,0,1,0,0,0,0,2
+City center nearby the beach +3Bed +2Bath +Wifi,Las Palmas de Gran Canaria,8.9,9.3,Three-Bedroom Apartment,"4 beds (2 twins, 2 fulls)",347.377,Las Palmas,Spain,2,2,0,0,0,0,0,4
+"Casa Rural Herencia de Ganaderos ""Sierra de Gredos- Ávila""",San Martín de la Vega del Alberche,9.4,9.6,Two-Bedroom House,"3 beds (1 twin, 2 fulls)",511.334,San Martín de la Vega del Alberche,Spain,1,2,0,0,0,0,0,3
+Seafront recently renovated apartments,Benalmádena,8.6,9.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",329.545,Benalmádena,Spain,2,1,0,0,0,0,0,4
+Apartaments Colibri,Cambrils,8.8,8.8,Apartment - Ground Floor,"6 beds (3 twins, 2 bunk beds, 1 queen)",275.588,Cambrils,Spain,3,0,1,0,0,2,0,6
+Heliopolis,San Agustin,9.1,9.4,Apartment with Sea View,"3 beds (1 sofa bed, 2 queens)",186.24,San Agustin,Spain,0,0,2,0,0,0,0,3
+Apartamentos Cabrera,Puerto del Carmen,8.9,8.8,Studio Apartment,2 twin beds,223.952,Puerto del Carmen,Spain,2,0,0,0,0,0,0,2
+Apartahotel Líbere Vitoria,Vitoria-Gasteiz,8.5,8.9,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",194.366,Vitoria-Gasteiz,Spain,0,1,0,0,0,0,0,2
+Luz de Azahar Bed and Breakfast,Peniscola,9.2,9.6,Three-Bedroom Apartment (4-6 Adults),"4 beds (2 twins, 2 fulls)",194.942,Peniscola,Spain,2,2,0,0,0,0,0,4
+Coqueto estudio en Calle Vela,Benalmádena,8.1,8.1,Queen Studio,1 full bed,164.483,Benalmádena,Spain,0,1,0,0,0,0,0,1
+Aparthotel Novo Mar,Paguera,8.4,8.5,Two-Bedroom Apartment (3 Adults),Multiple bed types,544.443,Paguera,Spain,0,0,0,0,0,0,0,0
+Bungalows Santa Clara,Playa del Ingles,7.2,0.0,Two-Bedroom House,4 twin beds,153.691,Playa del Ingles,Spain,4,0,0,0,0,0,0,4
+Apartamento Vacacional con vistas al mar,Santa Cruz de Tenerife,9.1,9.2,Studio Apartment with Sea View,"2 beds (1 full, 1 sofa bed)",186.41400000000002,Santa Cruz de Tenerife,Spain,0,1,0,0,0,0,0,2
+"Maday Home , big terrace and swimingpool",Granadilla de Abona,8.8,9.2,Two-Bedroom Apartment,"2 beds (1 twin, 1 full)",173.621,Granadilla de Abona,Spain,1,1,0,0,0,0,0,2
+Barco Princess Cachucho Fly,Puerto Calero,0.0,0.0,Mobile Home,"3 beds (1 full, 2 sofa beds)",179.03,Puerto Calero,Spain,0,1,0,0,2,0,0,3
+CASA NANO,Santa María del Páramo,9.3,8.8,Three-Bedroom House,"5 beds (2 twins, 2 fulls, 1 sofa bed)",233.931,Santa María del Páramo,Spain,2,2,0,0,0,0,0,5
+APARTAMENTO NUEVO CON PISCINA EN EL CAMPO DE GOLF MOSA TRAJECTUM,Murcia,9.2,9.5,Two-Bedroom Apartment,"3 beds (2 twins, 1 queen)",272.107,Murcia,Spain,2,0,1,0,0,0,0,3
+Boí Taüll CALMA DÚPLEX,Taüll,9.0,10.0,Apartment with Mountain View,"6 beds (2 twins, 1 full, 2 bunk beds, 1 sofa bed)",508.243,Taüll,Spain,2,1,0,0,0,2,0,6
+Apartamento Aticoroel,Jaca,9.2,9.3,One-Bedroom Apartment,4 twin beds,292.414,Jaca,Spain,4,0,0,0,0,0,0,4
+GOLDEN TOWER pensión,Gandía,7.1,0.0,Double Room with Terrace,1 full bed,131.47,Gandía,Spain,0,1,0,0,0,0,0,1
+"Buhardilla en calle Ancha, pleno centro",Sanlúcar de Barrameda,9.4,10.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",262.244,Sanlúcar de Barrameda,Spain,0,1,0,0,0,0,0,2
+Casa Tomarén,San Bartolomé,8.1,8.0,Studio (2 Adults),1 queen bed,316.781,San Bartolomé,Spain,0,0,1,0,0,0,0,1
+Cas Comte Suites & Spa - Adults Only,Lloseta,8.6,9.2,Suite with Hot Tub,1 king bed,432.238,Lloseta,Spain,0,0,0,1,0,0,0,1
+InmoSantos Apartaments Nuria,"Santa Margarida, Roses",8.1,8.3,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",190.91400000000002,Roses,Spain,2,1,0,0,0,0,0,4
+Apartamentos Las Carabelas,Benidorm,7.9,0.0,One-Bedroom Apartment,"4 beds (2 twins, 2 sofa beds)",180.43800000000002,Benidorm,Spain,2,0,0,0,2,0,0,4
+Aparthotel Diamant Blue,Playas de Orihuela,6.6,0.0,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",207.03900000000002,Orihuela,Spain,2,1,0,0,0,0,0,3
+ConilPlus Apartment LORCA,"City Center, Conil de la Frontera",6.9,0.0,Three-Bedroom Apartment,"4 beds (2 twins, 2 fulls)",210.872,Conil de la Frontera,Spain,2,2,0,0,0,0,0,4
+Hotel Manises,Manises,6.1,0.0,Budget Twin Room,2 twin beds,105.59400000000001,Manises,Spain,2,0,0,0,0,0,0,2
+Egona -Apartamentos Itxaropena Zarautz,Zarautz,8.7,9.0,Apartment - Ground Floor,"3 beds (2 twins, 1 sofa bed)",381.414,Zarautz,Spain,2,0,0,0,0,0,0,3
+Hotel Avenida del Sotillo,La Lastrilla,7.3,0.0,Double or Twin Room,2 twin beds,182.294,La Lastrilla,Spain,2,0,0,0,0,0,0,2
+APARTAMENTOS EN EL CENTRO DE LOS CRISTIANOS,Arona,8.0,8.0,One-Bedroom Apartment,2 twin beds,122.281,Arona,Spain,2,0,0,0,0,0,0,2
+Hostal Juan Bravo,Segovia,7.5,0.0,Deluxe Double or Twin Room with City View,1 full bed,280.81,Segovia,Spain,0,1,0,0,0,0,0,1
+Camping Vila de Sarria,Sarria,9.0,9.3,Superior Bungalow,"2 beds (1 full, 1 sofa bed)",159.4,Sarria,Spain,0,1,0,0,0,0,0,2
+Estudio en Nueva Torrequebrada,Benalmádena,7.9,8.0,Superior Studio,1 full bed,155.101,Benalmádena,Spain,0,1,0,0,0,0,0,1
+Maison Lamic 1,"Lloret Town Center, Lloret de Mar",9.0,9.0,Apartment,"5 beds (1 full, 4 bunk beds)",195.186,Lloret de Mar,Spain,0,1,0,0,0,4,0,5
+Casas del Madroño,Cazalla de la Sierra,8.9,8.8,Holiday Home,"3 beds (2 twins, 1 full)",127.2,Cazalla de la Sierra,Spain,2,1,0,0,0,0,0,3
+JSM Apartamentos,Benalmádena,6.9,0.0,Superior Apartment,"3 beds (2 twins, 1 sofa bed)",253.541,Benalmádena,Spain,2,0,0,0,0,0,0,3
+Casa Guaranatura alojamiento y barrancos,Bierge,7.0,0.0,Triple Room with Shared Bathroom,"2 beds (1 twin, 1 full)",170.57500000000002,Bierge,Spain,1,1,0,0,0,0,0,2
+Best Club Vacaciones Pueblo Indalo,Mojácar,8.0,0.0,Two-Bedroom Apartment (5 Adults + 1 Child),"5 beds (4 twins, 1 sofa bed)",202.616,Mojácar,Spain,4,0,0,0,0,0,0,5
+Sercotel Sevilla Guadalquivir Suites,"Old Town, Seville",8.7,9.1,Superior Apartment,"2 beds (1 full, 1 sofa bed)",513.086,Seville,Spain,0,1,0,0,0,0,0,2
+Apartment estudi,Begur,9.0,9.4,One-Bedroom Apartment,1 full bed,128.105,Begur,Spain,0,1,0,0,0,0,0,1
+Casa de Aduanas,Vegadeo,7.6,0.0,Apartment - Ground Floor,"2 beds (1 full, 1 sofa bed)",174.636,Vegadeo,Spain,0,1,0,0,0,0,0,2
+El Mirador Del Parque,Gúa,7.2,0.0,Apartment,"4 beds (2 twins, 2 sofa beds)",170.57500000000002,Gúa,Spain,2,0,0,0,2,0,0,4
+Carving Surf Hostel,San Esteban de Pravia,8.7,8.4,Quadruple Room with Bathroom,4 bunk beds,248.552,San Esteban de Pravia,Spain,0,0,0,0,0,4,0,4
+LEÓN SUITE Urban Studios,"Leon City Centre, León",9.2,9.5,Standard Studio,   1 double or 2 twins,286.821,León,Spain,2,0,0,0,0,0,0,2
+Apartaments Lamoga - Boabi,Torredembarra,8.2,8.1,Apartment with Sea View,"3 beds (2 twins, 1 full)",184.221,Torredembarra,Spain,2,1,0,0,0,0,0,3
+New Leaf Cortijo Apartment Full disabled access,Moclín,9.8,10.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",279.621,Moclín,Spain,0,2,0,0,0,0,0,3
+Hotel Monarque Fuengirola Park,Fuengirola,7.4,0.0,Double Room (2 Adults + 1 Child ),3 twin beds,270.761,Fuengirola,Spain,3,0,0,0,0,0,0,3
+Sabino - baskeyrentals,Lekeitio,9.7,10.0,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",198.395,Lekeitio,Spain,2,1,0,0,0,0,0,3
+Casa Girasole,Corralejo,8.6,8.8,Two-Bedroom House,"3 beds (2 twins, 1 queen)",202.25300000000001,Corralejo,Spain,2,0,1,0,0,0,0,3
+NEVERENDING RIJANA. ¡Cala Rijana a tu alcance!,Calahonda,9.2,9.5,Apartment with Terrace,"4 beds (1 twin, 1 full, 1 sofa bed, 1 queen)",214.669,Calahonda,Spain,1,1,1,0,0,0,0,4
+Marina,Ribadesella,7.3,0.0,Double or Twin Room with Harbor View,   1 double or 2 twins,193.666,Ribadesella,Spain,2,0,0,0,0,0,0,2
+BUNGALOW en CAMPO INTERNACIONAL,Maspalomas,8.7,9.4,One-Bedroom House,"2 beds (1 full, 1 sofa bed)",147.947,Maspalomas,Spain,0,1,0,0,0,0,0,2
+Apartamentos Berlin,San Pedro del Pinatar,8.7,8.8,Queen Studio with Sofa Bed,"2 beds (1 full, 1 sofa bed)",134.023,San Pedro del Pinatar,Spain,0,1,0,0,0,0,0,2
+Casa Marsial,Eriste,8.0,0.0,Penthouse Apartment,"3 beds (2 twins, 1 sofa bed)",235.323,Eriste,Spain,2,0,0,0,0,0,0,3
+Habitaciones Sajorami Beach,Zahora,7.6,0.0,Special Double,1 full bed,278.002,Zahora,Spain,0,1,0,0,0,0,0,1
+Camping La Llosa,Cambrils,8.2,8.0,Bungalow,"4 beds (3 twins, 1 full)",231.842,Cambrils,Spain,3,1,0,0,0,0,0,4
+Playa Esperanza Resort,Playa de Muro,8.3,8.6,Apartment with Lateral Sea View,"4 beds (2 twins, 2 sofa beds)",584.827,Playa de Muro,Spain,2,0,0,0,2,0,0,4
+Estudio Costa Cálida by Rental Olé,El Mojón,1.0,0.0,Apartment,"3 beds (1 queen, 2 futons)",111.535,El Mojón,Spain,0,0,1,0,0,0,0,3
+Kampaoh Paloma,Tarifa,8.3,8.3,Buka Tent,1 full bed,151.133,Tarifa,Spain,0,1,0,0,0,0,0,1
+Apartamentos Tanajara,La Restinga,7.6,0.0,Apartment,2 twin beds,121.839,La Restinga,Spain,2,0,0,0,0,0,0,2
+Habitaciones Casa de Campo Daymipaz,Mahón,7.3,0.0,Duplex Suite,1 queen bed,236.716,Mahón,Spain,0,0,1,0,0,0,0,1
+Albergue de Prioro,Prioro,9.2,9.3,Quadruple Room with Shared Bathroom,"4 beds (2 twins, 2 bunk beds)",188.119,Prioro,Spain,2,0,0,0,0,2,0,4
+Aparthotel Marina Drach,Porto Cristo,8.5,8.8,Studio,2 twin beds,318.0,Porto Cristo,Spain,2,0,0,0,0,0,0,2
+S´Estancia Suites,Es Mercadal,9.4,9.7,Double or Twin Room,   1 double or 2 twins,329.279,Es Mercadal,Spain,2,0,0,0,0,0,0,2
+Casa Rural Cordobelas,Cedeira,9.2,9.4,Double or Twin Room with Private Bathroom,   1 double or 2 twins,141.333,Cedeira,Spain,2,0,0,0,0,0,0,2
+Cabanas de Vendaval,Malpica,9.7,9.8,Mobile Home,"4 beds (2 twins, 1 full, 1 sofa bed)",288.932,Malpica,Spain,2,1,0,0,0,0,0,4
+Casa Endorfina Rooms,Los Cristianos,7.9,8.3,Standard Double Room,   1 double or 2 twins,209.987,Los Cristianos,Spain,2,0,0,0,0,0,0,2
+Hotel Rural Albamanjon,Ruidera,9.1,9.0,Double Room with Spa Bath,1 full bed,375.508,Ruidera,Spain,0,1,0,0,0,0,0,1
+Camping Ampolla Playa,L'Ampolla,8.7,8.7,Bungalow FARO ( 2 p. ),"3 beds (2 twins, 1 full)",275.008,L'Ampolla,Spain,2,1,0,0,0,0,0,3
+Kampea Playa de Gandia,Gandía,7.4,0.0,Tent,1 full bed,122.187,Gandía,Spain,0,1,0,0,0,0,0,1
+Apartamentos OlaMar,Lloret de Mar,8.8,9.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",146.207,Lloret de Mar,Spain,0,1,0,0,0,0,0,2
+Casa Albano,Baldellou,8.7,8.3,Deluxe Family Room,Multiple bed types,385.986,Baldellou,Spain,0,0,0,0,0,0,0,0
+Medplaya Hotel Vistamar Costa Dorada,Hospitalet de l'Infant,7.8,0.0,Double Room with Front Sea View,"2 beds (1 twin, 1 full)",192.058,Hospitalet de l'Infant,Spain,1,1,0,0,0,0,0,2
+Casa Rural Ca Xelo 2,Poliñá de Júcar,7.0,10.0,Double Room,1 twin bed,165.701,Poliñá de Júcar,Spain,1,0,0,0,0,0,0,1
+Aparthotel Carrio Sol - Monty´s,Calpe,8.5,8.6,Studio (4 Adults),"2 beds (1 full, 1 sofa bed)",263.46500000000003,Calpe,Spain,0,1,0,0,0,0,0,2
+Apartamentos Palm Garden by LIVVO,Morro del Jable,7.5,0.0,Studio with Balcony with Sea View (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",158.293,Morro del Jable,Spain,2,0,0,0,0,0,0,3
+Hotel del Port,L'Ametlla de Mar,7.9,8.1,Double or Twin Room,   1 double or 2 twins,184.499,L'Ametlla de Mar,Spain,2,0,0,0,0,0,0,2
+ÁTICO ESTRELLA,Tejina de Isora,9.1,8.7,Apartment,"2 beds (1 full, 1 sofa bed)",109.655,Tejina de Isora,Spain,0,1,0,0,0,0,0,2
+La Quinta II by HOMA,Isla Canela,8.3,8.5,Apartment - Ground Floor,"3 beds (2 twins, 1 full)",120.62100000000001,Isla Canela,Spain,2,1,0,0,0,0,0,3
+Raco d'en Pepe,Calella,6.8,0.0,Twin Room,2 twin beds,136.46,Calella,Spain,2,0,0,0,0,0,0,2
+Posada El Labrador,San Vicente de la Barquera,8.7,8.7,Double or Twin Room,   1 double or 2 twins,134.023,San Vicente de la Barquera,Spain,2,0,0,0,0,0,0,2
+Hostería Casa Flor,Murias de Rechivaldo,8.7,8.9,Twin Room,2 twin beds,131.934,Murias de Rechivaldo,Spain,2,0,0,0,0,0,0,2
+Pensión Mariola,Agres,8.7,8.7,Two-Bedroom House,"6 beds (3 twins, 2 fulls, 1 sofa bed)",351.418,Agres,Spain,3,2,0,0,0,0,0,6
+Pensión Venus,Nigrán,8.4,8.7,Double or Twin Room,   1 double or 2 twins,119.959,Nigrán,Spain,2,0,0,0,0,0,0,2
+Cal Alejandro,Montsonis,7.6,0.0,Double Room,1 full bed,202.25300000000001,Montsonis,Spain,0,1,0,0,0,0,0,1
+Hostal la Hispanidad,"Malaga Centro, Málaga",7.3,0.0,Double or Twin Room,"3 beds (2 twins, 1 full)",245.767,Málaga,Spain,2,1,0,0,0,0,0,3
+Klayman Diamond Aparthotel,Acantilado de los Gigantes,9.1,9.3,One-Bedroom Apartment with Sea View,"4 beds (2 twins, 1 sofa bed, 1 queen)",416.591,Acantilado de los Gigantes,Spain,2,0,1,0,0,0,0,4
+Apartment La Gomera II,San Juan de la Rambla,7.8,0.0,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",219.31,San Juan de la Rambla,Spain,2,1,0,0,0,0,0,3
+Alojamiento rural la Bellosina,Cabezabellosa,8.1,8.0,Double or Twin Room,   1 double or 2 twins,97.471,Cabezabellosa,Spain,2,0,0,0,0,0,0,2
+Hotel Pirineos,Castejón de Sos,8.6,8.8,Triple Room,Multiple bed types,190.06900000000002,Castejón de Sos,Spain,0,0,0,0,0,0,0,0
+Camino de Estrellas,Mondoñedo,8.6,8.8,Quadruple Room with Balcony,"3 beds (2 twins, 1 full)",214.437,Mondoñedo,Spain,2,1,0,0,0,0,0,3
+Hostal Algeciras,Algeciras,5.5,0.0,Twin Room,2 twin beds,95.034,Algeciras,Spain,2,0,0,0,0,0,0,2
+Vivienda Turística Pirineos XXI,Canfranc-Estación,8.1,0.0,Apartment,"5 beds (4 twins, 1 sofa bed)",175.071,Canfranc-Estación,Spain,4,0,0,0,0,0,0,5
+Casa Rural La Cabrera,Robledo del Buey,9.0,9.4,Twin Room,2 twin beds,133.211,Robledo del Buey,Spain,2,0,0,0,0,0,0,2
+Hostal Soto,La Virgen del Camino,8.3,8.4,Double Room,1 full bed,166.165,La Virgen del Camino,Spain,0,1,0,0,0,0,0,1
+Hotel Nou Can Guillem,Cala Figuera,8.5,9.1,Deluxe Twin Room,2 twin beds,284.059,Cala Figuera,Spain,2,0,0,0,0,0,0,2
+La Charra,Sanxenxo,6.1,0.0,Double Room,1 full bed,153.169,Sanxenxo,Spain,0,1,0,0,0,0,0,1
+Hostal SolFina,Palafolls,6.3,0.0,Double Room,1 full bed,146.207,Palafolls,Spain,0,1,0,0,0,0,0,1
+PR As Termas,Bubaces,8.5,8.5,Double Room,   1 double or 2 twins,121.839,Bubaces,Spain,2,0,0,0,0,0,0,2
+La Cerca,Arcones,7.8,0.0,Standard Queen Room,1 full bed,184.499,Arcones,Spain,0,1,0,0,0,0,0,1
+La Fustana,Maia de Montcal,9.1,9.6,Duplex Room,2 queen beds,233.931,Maia de Montcal,Spain,0,0,2,0,0,0,0,2
+PORTO MAR,Bares,8.3,8.0,Double Room,1 full bed,85.287,Bares,Spain,0,1,0,0,0,0,0,1
+El Gato Gordo - Riders Hostel,Cudillero,8.7,9.0,Double Room with Shared Bathroom,1 full bed,168.138,Cudillero,Spain,0,1,0,0,0,0,0,1
+Cases Altes de Posada,Navés,9.1,9.1,Deluxe Suite,"5 beds (2 twins, 1 bunk bed, 1 sofa bed, 1 queen)",558.458,Navés,Spain,2,0,1,0,0,0,0,5
+Hotel Cordial Mogán Playa,Puerto de Mogán,8.9,9.3,Double or Twin Room,2 twin beds,381.892,Puerto de Mogán,Spain,2,0,0,0,0,0,0,2
+Poseidon La Manga Hotel & Spa - Designed for Adults,La Manga del Mar Menor,8.1,8.4,Double or Twin Room,   1 double or 2 twins,156.65,La Manga del Mar Menor,Spain,2,0,0,0,0,0,0,2
+Moon Dreams Calabahía,Cala del Moral,8.0,8.4,Double or Twin Room,   1 double or 2 twins,209.917,Cala del Moral,Spain,2,0,0,0,0,0,0,2
+"Villa Le Blanc, a Gran Meliá Hotel - The Leading Hotels of The World",Santo Tomás,9.2,9.6,Deluxe Room with Terrace,   1 double or 2 twins,788.124,Santo Tomás,Spain,2,0,0,0,0,0,0,2
+Apartamentos Acuario Sol,Puerto del Carmen,8.6,8.7,Two-Bedroom Apartment,Multiple bed types,319.56600000000003,Puerto del Carmen,Spain,0,0,0,0,0,0,0,0
+Hotel Best Costa Ballena,Costa Ballena,8.6,9.1,Double Room,2 full beds,257.496,Costa Ballena,Spain,0,2,0,0,0,0,0,2
+Aparthotel Golf Beach,Pals,8.2,8.3,Apartment with Garden View,"4 beds (2 twins, 2 sofa beds)",209.56300000000002,Pals,Spain,2,0,0,0,2,0,0,4
+Mogan Princess & Beach Club,Taurito,7.9,8.0,Double Room with Ocean View,2 twin beds,385.526,Taurito,Spain,2,0,0,0,0,0,0,2
+Puertobahia & SPA,El Puerto de Santa María,8.1,8.4,Double or Twin Room with Sea View,   1 double or 2 twins,265.145,El Puerto de Santa María,Spain,2,0,0,0,0,0,0,2
+Sercotel Gran Hotel Zurbarán,Badajoz,8.3,8.7,Double Room,1 full bed,245.186,Badajoz,Spain,0,1,0,0,0,0,0,1
+Camping Bungalow Serra de Prades Resort,Vilanova de Prades,8.4,8.2,Deluxe Bungalow,2 full beds,374.22,Vilanova de Prades,Spain,0,2,0,0,0,0,0,2
+Hotel Colonial Mar,Roquetas de Mar,7.3,0.0,Double or Twin Room,   1 double or 2 twins,171.387,Roquetas de Mar,Spain,2,0,0,0,0,0,0,2
+Ilunion Alcora Sevilla,San Juan de Aznalfarache,8.6,8.8,Double Room Pet Friendly,2 twin beds,500.57300000000004,San Juan de Aznalfarache,Spain,2,0,0,0,0,0,0,2
+Casa Álamo Agaete,Agaete,8.0,8.0,Double Room with Mountain View,1 full bed,113.83200000000001,Agaete,Spain,0,1,0,0,0,0,0,1
+Silken Ciudad de Vitoria,Vitoria-Gasteiz,8.6,9.0,   Twin Room,2 twin beds,234.546,Vitoria-Gasteiz,Spain,2,0,0,0,0,0,0,2
+Hacienda del Conde member of Meliá Collection - Adults Only,Buenavista del Norte,9.0,9.5,Singular Junior Suite,2 twin beds,723.492,Buenavista del Norte,Spain,2,0,0,0,0,0,0,2
+Royal Palm Resort & Spa - Adults Only,Playa Jandia,8.4,9.1,Double or Twin Room with Side Sea View,   1 double or 2 twins,387.991,Playa Jandia,Spain,2,0,0,0,0,0,0,2
+Pierre & Vacances Resort Terrazas Costa del Sol,Manilva,7.2,0.0,One-Bedroom Apartment,"3 beds (1 full, 2 sofa beds)",179.696,Manilva,Spain,0,1,0,0,2,0,0,3
+Hospedium Hotel Mirador de Gredos,El Barco de Ávila,8.0,8.5,Deluxe Double Room,1 king bed,352.40500000000003,El Barco de Ávila,Spain,0,0,0,1,0,0,0,1
+Hotel Vila Centric,Vilaseca de Solcina,8.0,8.3,Standard Double Room 2 adults,1 full bed,192.041,Vilaseca de Solcina,Spain,0,1,0,0,0,0,0,1
+Apartamentos Cordial Mogán Valle,Puerto de Mogán,8.4,8.5,Superior One-Bedroom Apartment,2 twin beds,242.298,Puerto de Mogán,Spain,2,0,0,0,0,0,0,2
+Hesperia Murcia Centro,Murcia,8.6,8.9,Standard Double or Twin Room,   1 double or 2 twins,223.48600000000002,Murcia,Spain,2,0,0,0,0,0,0,2
+Tramuntana,La Jonquera,8.7,9.0,Double or Twin Room,   1 double or 2 twins,171.387,La Jonquera,Spain,2,0,0,0,0,0,0,2
+Elba Motril Beach & Business Hotel,Motril,8.3,8.7,Standard Family Room (2 Adults + 1 Children),Multiple bed types,260.735,Motril,Spain,0,0,0,0,0,0,0,0
+Complejo Residencial Los Enebros,Arroyo Frio,8.9,8.9,Two-Bedroom Bungalow (4 Adults),2 full beds,402.069,Arroyo Frio,Spain,0,2,0,0,0,0,0,2
+Hotel Cap Roig by Brava Hoteles,Platja d'Aro,8.1,8.5,Standard Triple Room,3 twin beds,368.418,Platja d'Aro,Spain,3,0,0,0,0,0,0,3
+Hotel Santiago,Benavente,8.4,8.8,Double or Twin Room,   1 double or 2 twins,165.701,Benavente,Spain,2,0,0,0,0,0,0,2
+Alannia Costa Dorada,Platja de l’Almadrava,7.9,0.0,Three-Bedroom Bungalow,"5 beds (4 twins, 1 full)",208.055,Platja de l’Almadrava,Spain,4,1,0,0,0,0,0,5
+Eden Roc Mediterranean Hotel & Spa,Sant Feliu de Guíxols,8.1,8.6,Superior Double or Twin Room with Sea View,2 twin beds,393.366,Sant Feliu de Guíxols,Spain,2,0,0,0,0,0,0,2
+Hotel Ripoll,Sant Hilari Sacalm,8.1,8.6,Basic Double Room,   1 double or 2 twins,164.077,Sant Hilari Sacalm,Spain,2,0,0,0,0,0,0,2
+Elba Costa Ballena Beach & Thalasso Resort,Costa Ballena,8.5,8.8,Family Room (2 Adults + 2 Children),Multiple bed types,330.558,Costa Ballena,Spain,0,0,0,0,0,0,0,0
+Puerto Antilla Grand Hotel,Islantilla,8.9,9.1,Twin Room (2 Adults + 1 Child),3 twin beds,244.838,Islantilla,Spain,3,0,0,0,0,0,0,3
+El Vicenç de la Mar - Adults Only - Over 12,Cala de Sant Vicenc,9.0,9.7,Superior Double Room with Shared Terrace,   1 double or 2 twins,851.506,Cala de Sant Vicenc,Spain,2,0,0,0,0,0,0,2
+Ona Valle Romano Golf & Resort,Estepona,8.4,8.7,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",230.088,Estepona,Spain,2,1,0,0,0,0,0,4
+Marina Elite,Patalavaca,7.3,0.0,One-Bedroom Apartment Sea View,"3 beds (2 twins, 1 sofa bed)",265.226,Patalavaca,Spain,2,0,0,0,0,0,0,3
+Villa Healthy,Mogán,9.0,10.0,Two-Bedroom Villa,2 full beds,285.103,Mogán,Spain,0,2,0,0,0,0,0,2
+Axel Hotel Madrid - Adults Only,"Madrid City Center, Madrid",8.5,8.9,Double or Twin Room,   1 double or 2 twins,590.992,Madrid,Spain,2,0,0,0,0,0,0,2
+Santa Barbara Golf and Ocean Club,San Miguel de Abona,8.5,9.1,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",245.419,San Miguel de Abona,Spain,2,0,0,0,0,0,0,3
+Hotel Princesa Playa,Son Xoriguer,8.6,8.9,Double Room,   1 double or 2 twins,249.596,Son Xoriguer,Spain,2,0,0,0,0,0,0,2
+Hotel Petit Sagitario (Adults only),Son Carrio,9.2,9.4,Standard Double or Twin Room with Balcony,   1 double or 2 twins,398.93600000000004,Son Carrio,Spain,2,0,0,0,0,0,0,2
+Fuerteventura Princess,Playa Jandia,8.3,8.7,Family Room (2 Adults + 1 Child),"2 beds (1 full, 1 sofa bed)",745.455,Playa Jandia,Spain,0,1,0,0,0,0,0,2
+Apartamentos Alday,Maliaño,8.4,8.5,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",219.31,Maliaño,Spain,0,1,0,0,0,0,0,2
+Aparthotel Cordial Mijas Golf,Mijas,7.7,8.0,Two-bedroom Apartment Superior,"3 beds (1 sofa bed, 2 queens)",301.81600000000003,Mijas,Spain,0,0,2,0,0,0,0,3
+Hotel Pax Guadalajara,Guadalajara,8.7,9.2,Two-Bedroom Suite with Sofa Bed,"2 beds (1 king, 1 sofa bed)",407.29,Guadalajara,Spain,0,0,0,1,0,0,0,2
+Hotel Badajoz Center,Badajoz,8.6,9.1,Double or Twin Room,2 twin beds,215.707,Badajoz,Spain,2,0,0,0,0,0,0,2
+Apartamentos Casa La Rambla,Muros de Nalón,9.4,9.4,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",353.591,Muros de Nalón,Spain,2,1,0,0,0,0,0,4
+Esencia de La Palma by Princess - Adults Only,Fuencaliente de la Palma,8.2,8.4,Double Room,2 twin beds,337.408,Fuencaliente de la Palma,Spain,2,0,0,0,0,0,0,2
+Camping Resort-Bungalow Park Mas Patoxas,Pals,7.9,0.0,Superior Two-Bedroom Bungalow,"4 beds (1 full, 1 bunk bed, 1 sofa bed, 1 queen)",385.011,Pals,Spain,0,1,1,0,0,0,0,4
+ON City Resort,Matalascañas,8.6,9.0,Apartment - Ground Floor,"2 beds (1 full, 1 sofa bed)",258.387,Matalascañas,Spain,0,1,0,0,0,0,0,2
+Hotel Adaria Vera,Vera,8.1,8.4,Double or Twin Room,2 twin beds,222.791,Vera,Spain,2,0,0,0,0,0,0,2
+Occidental Isla Cristina,Isla Cristina,8.3,8.9,Deluxe One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",313.469,Isla Cristina,Spain,2,0,0,0,0,0,0,3
+ON The Residence,Matalascañas,8.9,9.2,Double Room,1 queen bed,198.772,Matalascañas,Spain,0,0,1,0,0,0,0,1
+Pierre & Vacances Resort Fuerteventura OrigoMare,Lajares,7.7,8.0,Standard Studio (2 Adults + 1 Child),"2 beds (1 full, 1 sofa bed)",156.788,Lajares,Spain,0,1,0,0,0,0,0,2
+Elba Lucía Sport & Suite Hotel,Costa de Antigua,7.3,0.0,One-Bedroom Apartment (2 Adults + 1 Child),2 twin beds,154.631,Costa de Antigua,Spain,2,0,0,0,0,0,0,2
+Ilunion Islantilla,Islantilla,8.4,8.7,Twin Room,2 twin beds,218.76500000000001,Islantilla,Spain,2,0,0,0,0,0,0,2
+Hotel New Bilbao Airport,Derio,8.5,9.2,Double or Twin Room,   1 double or 2 twins,233.119,Derio,Spain,2,0,0,0,0,0,0,2
+Blue Ocean Camp - Tasartico,Tasartico,7.7,0.0,Mini Bungalow (No Kitchen and Bathroom),1 full bed,108.031,Tasartico,Spain,0,1,0,0,0,0,0,1
+Palacio de Luja,El Puerto de Santa María,8.2,8.3,Standard Apartment (2 Adults),2 twin beds,272.896,El Puerto de Santa María,Spain,2,0,0,0,0,0,0,2
+Ronda Navarra by Valdesierra,Béjar,8.1,8.2,Studio (2 Adults),1 full bed,114.761,Béjar,Spain,0,1,0,0,0,0,0,1
+Hotel Doña Brígida – Salamanca Forum,Villamayor,8.1,8.5,Double Room,Multiple bed types,190.30100000000002,Villamayor,Spain,0,0,0,0,0,0,0,0
+Hotel Segontia,Épila,7.4,0.0,Double Room,1 full bed,131.586,Épila,Spain,0,1,0,0,0,0,0,1
+Real Ferrol,Ferrol,7.1,0.0,Standard Twin Room,2 twin beds,137.216,Ferrol,Spain,2,0,0,0,0,0,0,2
+Exe Gran Hotel Almenar,Las Rozas de Madrid,8.5,8.9,Double or Twin Room,   1 double or 2 twins,228.709,Las Rozas de Madrid,Spain,2,0,0,0,0,0,0,2
+Santemar,Santander,8.8,9.3,Junior Suite,1 queen bed,736.836,Santander,Spain,0,0,1,0,0,0,0,1
+Hotel Lanjaron,Lanjarón,7.6,0.0,Deluxe Double Room with Balcony,   1 double or 2 twins,102.113,Lanjarón,Spain,2,0,0,0,0,0,0,2
+Ilunion Golf Badajoz,Badajoz,8.1,8.5,Double or Twin Room,   1 double or 2 twins,192.11,Badajoz,Spain,2,0,0,0,0,0,0,2
+Destinos de Sol La Minería Roquetas de Mar,Roquetas de Mar,7.6,0.0,Superior Apartment with Terrace and Sea View,"3 beds (2 twins, 1 sofa bed)",100.147,Roquetas de Mar,Spain,2,0,0,0,0,0,0,3
+Casa Rural Finca Paraíso,Valle de Guerra,9.0,9.2,Deluxe Double or Twin Room with Pool Access,1 full bed,234.047,Valle de Guerra,Spain,0,1,0,0,0,0,0,1
+Complejo Rural La Glorieta,Catí,8.4,8.2,Apartment,"3 beds (2 twins, 1 full)",152.589,Catí,Spain,2,1,0,0,0,0,0,3
+Hotel Pozo del Duque,Zahara de los Atunes,8.0,8.1,Double or Twin Room with Side Sea View,2 twin beds,257.60200000000003,Zahara de los Atunes,Spain,2,0,0,0,0,0,0,2
+Ona Ogisaka Garden,Denia,8.4,8.5,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",244.142,Denia,Spain,0,1,0,0,0,0,0,2
+La Familia Gallo Rojo,El Campello,8.0,8.2,Twin Room,2 twin beds,160.131,El Campello,Spain,2,0,0,0,0,0,0,2
+Ocean Lounge,Altea,8.2,8.8,Suite with Terrace,1 queen bed,402.069,Altea,Spain,0,0,1,0,0,0,0,1
+Hotel Thalasso Cantabrico Las Sirenas,"Covas, Viveiro",8.0,8.6,Double or Twin Room,   1 double or 2 twins,153.964,Viveiro,Spain,2,0,0,0,0,0,0,2
+Hotel Saurat,Espot,8.6,8.8,Twin Room,2 twin beds,184.731,Espot,Spain,2,0,0,0,0,0,0,2
+ALEGRIA Palacio Mojacar Adults only,Mojácar,8.7,9.0,Double Room with Sea View,"3 beds (2 twins, 1 full)",252.497,Mojácar,Spain,2,1,0,0,0,0,0,3
+Apartamentos Playa Barbate,Barbate,8.1,8.0,Two-Bedroom Apartment - Ground Floor,4 twin beds,292.994,Barbate,Spain,4,0,0,0,0,0,0,4
+Elba Vecindario Aeropuerto Business & Convention Hotel,Vecindario,8.3,8.7,Double or Twin Room (2 Adults),Multiple bed types,208.867,Vecindario,Spain,0,0,0,0,0,0,0,0
+Exe Victoria Palace,San Lorenzo de El Escorial,8.1,8.6,Standard Double or Twin Room,   1 double or 2 twins,245.651,San Lorenzo de El Escorial,Spain,2,0,0,0,0,0,0,2
+Hotel Cooee Taimar,Costa Calma,8.6,9.2,Junior Suite,"3 beds (2 twins, 1 sofa bed)",307.034,Costa Calma,Spain,2,0,0,0,0,0,0,3
+Hipotels Eurotel Punta Rotja Spa-Golf,Costa dels Pins,8.6,8.9,Superior Double Room,   1 double or 2 twins,349.079,Costa dels Pins,Spain,2,0,0,0,0,0,0,2
+La Pergola Mallorca,Port d'Andratx,8.2,8.6,Standard Double Room (2 Adults),2 twin beds,322.427,Port d'Andratx,Spain,2,0,0,0,0,0,0,2
+"AC Hotel Guadalajara by Marriott, Spain",Guadalajara,8.3,8.7,Twin Room,2 twin beds,224.585,Guadalajara,Spain,2,0,0,0,0,0,0,2
+Vincci Resort Costa Golf,"Novo Sancti Petri, Chiclana de la Frontera",8.8,9.1,Junior Suite (2 Adults),"3 beds (2 twins, 1 sofa bed)",235.497,Chiclana de la Frontera,Spain,2,0,0,0,0,0,0,3
+Hotel Playas de Guardamar,Guardamar del Segura,7.4,0.0,Double or Twin Room,   1 double or 2 twins,148.428,Guardamar del Segura,Spain,2,0,0,0,0,0,0,2
+Aparthotel Ona Cala Pi Club,Cala Pi,8.8,8.9,One-Bedroom Apartment (2 Adults),"2 beds (1 full, 1 sofa bed)",304.277,Cala Pi,Spain,0,1,0,0,0,0,0,2
+White Sands Beach Club,Arenal d'en Castell,8.9,9.1,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",394.091,Arenal d'en Castell,Spain,2,1,0,0,0,0,0,4
+Senator Mar Menor Golf & Spa Resort,Los Alcázares,7.4,0.0,One-Bedroom Apartment (2 Adults),"2 beds (1 sofa bed, 1 queen)",128.221,Los Alcázares,Spain,0,0,1,0,0,0,0,2
+Hotel ParqueMar Premium Beach,Guardamar del Segura,7.3,0.0,Deluxe Double Room with Balcony,   1 double or 2 twins,124.392,Guardamar del Segura,Spain,2,0,0,0,0,0,0,2
+Valentin Sancti Petri,"Novo Sancti Petri, Chiclana de la Frontera",8.9,9.1,Double or Twin Room,   1 double or 2 twins,298.54,Chiclana de la Frontera,Spain,2,0,0,0,0,0,0,2
+Eurostars Palacio de Santa Marta,Trujillo,8.8,9.1,Standard Twin Room,2 twin beds,326.877,Trujillo,Spain,2,0,0,0,0,0,0,2
+Ilunion Calas de Conil,Conil de la Frontera,8.5,8.9,Double or Twin Room,Multiple bed types,230.601,Conil de la Frontera,Spain,0,0,0,0,0,0,0,0
+Soho Boutique Puerto,El Puerto de Santa María,8.3,8.6,Standard Double Room,2 twin beds,357.51,El Puerto de Santa María,Spain,2,0,0,0,0,0,0,2
+Gara Suites Golf & Spa,Playa de las Americas,7.9,8.3,Standard Suite,"3 beds (2 twins, 1 sofa bed)",227.839,Playa de las Americas,Spain,2,0,0,0,0,0,0,3
+Hotel Antik San Sebastián,"Antiguo, San Sebastián",8.2,8.8,Double or Twin Room with Free Parking,Multiple bed types,333.473,San Sebastián,Spain,0,0,0,0,0,0,0,0
+Barceló Lanzarote Active Resort,Costa Teguise,8.2,8.7,Double or Twin Room,Multiple bed types,346.302,Costa Teguise,Spain,0,0,0,0,0,0,0,0
+Grupotel Flamingo Beach,Playa Blanca,8.3,8.3,Standard Apartment (2 Adults),2 twin beds,229.023,Playa Blanca,Spain,2,0,0,0,0,0,0,2
+"Radisson Blu Resort & Spa, Gran Canaria Mogan",Puerto de Mogán,8.8,9.1,Superior Room Pool View,Multiple bed types,436.532,Puerto de Mogán,Spain,0,0,0,0,0,0,0,0
+Hotel Apartamentos Londres La Manga,La Manga del Mar Menor,7.4,0.0,Standard Studio,   1 double or 2 twins,169.584,La Manga del Mar Menor,Spain,2,0,0,0,0,0,0,2
+Hotel Montjoi by Brava Hoteles,Sant Feliu de Guíxols,8.1,8.5,Budget Double or Twin Room,   1 double or 2 twins,196.799,Sant Feliu de Guíxols,Spain,2,0,0,0,0,0,0,2
+Vista Alegre,Benicàssim,8.1,8.0,Standard Double or Twin Room,   1 double or 2 twins,149.224,Benicàssim,Spain,2,0,0,0,0,0,0,2
+Plaza de la Luz Cádiz,"Old Town, Cádiz",8.9,9.2,Studio,1 full bed,267.535,Cádiz,Spain,0,1,0,0,0,0,0,1
+ICON Valparaiso - Adults Only,Cala Murada,8.6,8.8,Double Room,   1 double or 2 twins,426.582,Cala Murada,Spain,2,0,0,0,0,0,0,2
+Aparthotel Areu,Areu,8.9,9.2,Two-Bedroom Apartment (4 Adults),"3 beds (2 twins, 1 full)",279.621,Areu,Spain,2,1,0,0,0,0,0,3
+NH Canciller Ayala Vitoria,Vitoria-Gasteiz,8.4,8.8,Standard Double or Twin Room with View,   1 double or 2 twins,261.377,Vitoria-Gasteiz,Spain,2,0,0,0,0,0,0,2
+Hotel Meson de Erosa,Herosa,7.7,8.1,Double Room,2 twin beds,121.839,Herosa,Spain,2,0,0,0,0,0,0,2
+Alua Village Fuerteventura - All Inclusive,Playa Jandia,8.1,8.8,Standard Double Room (2 Adults),2 twin beds,377.701,Playa Jandia,Spain,2,0,0,0,0,0,0,2
+Kn Aparthotel Columbus,Playa de las Americas,7.6,0.0,Double or Twin Room (2 Adults),2 twin beds,408.60200000000003,Playa de las Americas,Spain,2,0,0,0,0,0,0,2
+Pierre & Vacances Estartit Playa,L'Estartit,8.1,8.3,One-Bedroom Apartment (4 Adults),"3 beds (2 twins, 1 sofa bed)",149.369,L'Estartit,Spain,2,0,0,0,0,0,0,3
+Hotel Colón Gran Meliá - The Leading Hotels of the World,"Old Town, Seville",9.1,9.5,Penthouse Suite Red Level,1 king bed,4065.292,Seville,Spain,0,0,0,1,0,0,0,1
+Cortijo Del Mar Resort,Estepona,8.8,9.1,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",439.16,Estepona,Spain,2,1,0,0,0,0,0,3
+Cala Llenya Resort Ibiza,Cala Llenya,8.3,8.6,Double Room,1 queen bed,505.661,Cala Llenya,Spain,0,0,1,0,0,0,0,1
+Ona Los Claveles,Los Cristianos,8.5,8.7,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",175.615,Los Cristianos,Spain,0,1,0,0,0,0,0,2
+Travelodge Valencia Aeropuerto,Manises,7.7,8.1,Double Room,1 full bed,182.616,Manises,Spain,0,1,0,0,0,0,0,1
+30º Hotels - Hotel Dos Playas Mazarrón,Puerto de Mazarrón,8.6,9.1,Deluxe Twin Room with Pool View,2 twin beds,233.119,Puerto de Mazarrón,Spain,2,0,0,0,0,0,0,2
+NH Ciudad Real,Ciudad Real,7.8,8.3,Superior Double or Twin Room,   1 double or 2 twins,295.435,Ciudad Real,Spain,2,0,0,0,0,0,0,2
+El Bulín de Piñuecar,Piñuécar,8.2,8.1,One-Bedroom House,1 full bed,228.808,Piñuécar,Spain,0,1,0,0,0,0,0,1
+Hotel Playa Mondrago,Portopetro,8.1,8.2,Superior Twin Room with Sea View,2 twin beds,443.564,Portopetro,Spain,2,0,0,0,0,0,0,2
+Vacaciones Oromarina Azahar,"Marina d’Or Holiday Resort Area, Oropesa del Mar",7.5,0.0,Two-Bedroom Apartment with Terrace and Parking,"4 beds (2 twins, 1 full, 1 sofa bed)",220.006,Oropesa del Mar,Spain,2,1,0,0,0,0,0,4
+Sol Arona Tenerife,Los Cristianos,7.3,0.0,Room with separate lounge,2 twin beds,263.056,Los Cristianos,Spain,2,0,0,0,0,0,0,2
+Parques Casablanca,"Benissa Coast, Benissa",9.0,9.2,Two-Bedroom Apartment,2 full beds,278.76800000000003,Benissa,Spain,0,2,0,0,0,0,0,2
+Dreams Lanzarote Playa Dorada Resort & Spa,Playa Blanca,8.3,9.2,Preferred Club Double Room with Pool View,   1 double or 2 twins,564.173,Playa Blanca,Spain,2,0,0,0,0,0,0,2
+"Holiday Inn Express Bilbao Airport, an IHG Hotel",Derio,7.8,8.2,Double or Twin Room,   1 double or 2 twins,263.868,Derio,Spain,2,0,0,0,0,0,0,2
+Aparthotel Esquinzo Y Monte Del Mar,Playa Jandia,7.3,0.0,Studio,2 twin beds,150.022,Playa Jandia,Spain,2,0,0,0,0,0,0,2
+Apartments Kione Playa Romana Park,Alcossebre,7.8,0.0,Two-Bedroom Apartment (6 Adults + 2 Children),"6 beds (4 twins, 2 sofa beds)",183.872,Alcossebre,Spain,4,0,0,0,2,0,0,6
+Palacio de los Velada,"Old Town of Avila, Ávila",8.5,8.7,Double Room,   1 double or 2 twins,212.58,Ávila,Spain,2,0,0,0,0,0,0,2
+Hotel Villa San Juan,San Juan de Alicante,8.1,8.4,Double or Twin Room,   1 double or 2 twins,168.834,San Juan de Alicante,Spain,2,0,0,0,0,0,0,2
+Hacienda FELIX,El Paso,8.9,8.9,Deluxe Family Room,"2 beds (1 full, 1 sofa bed)",250.014,El Paso,Spain,0,1,0,0,0,0,0,2
+Hotel El Puerto by Pierre Vacances,"El Castillo Beach, Fuengirola",7.3,0.0,Double Room with Sea View,2 twin beds,247.699,Fuengirola,Spain,2,0,0,0,0,0,0,2
+Sercotel Boulevard Vitoria-Gasteiz,Vitoria-Gasteiz,8.2,8.6,Junior Suite,   1 double or 2 twins,250.43800000000002,Vitoria-Gasteiz,Spain,2,0,0,0,0,0,0,2
+Grupotel Ibiza Beach Resort - Adults Only,Portinatx,8.4,8.6,Double Room with Side Sea View,2 twin beds,424.0,Portinatx,Spain,2,0,0,0,0,0,0,2
+Grupotel Maritimo,Port d'Alcudia,8.9,9.2,Double Room with Side Sea View (2 adults),2 twin beds,372.618,Port d'Alcudia,Spain,2,0,0,0,0,0,0,2
+Mon Port Hotel & Spa,Port d'Andratx,8.8,9.3,Double Standard Parking Spa access,1 king bed,508.732,Port d'Andratx,Spain,0,0,0,1,0,0,0,1
+Camping Platja Cambrils,Cambrils,7.8,0.0,One-Bedroom Bungalow (2 Adults),"2 beds (1 full, 1 sofa bed)",184.116,Cambrils,Spain,0,1,0,0,0,0,0,2
+Aptos. Comte D´Empuries,Empuriabrava,7.1,0.0,One-Bedroom Apartment (2 Adults + 2 Children),"4 beds (2 twins, 2 sofa beds)",193.898,Empuriabrava,Spain,2,0,0,0,2,0,0,4
+Apartamentos Candelario by gaiarooms,Candelario,8.5,8.6,Duplex Apartment,"2 beds (1 full, 1 sofa bed)",162.891,Candelario,Spain,0,1,0,0,0,0,0,2
+Apartamentos Sureda,Canyamel,9.1,9.3,One-Bedroom Apartment (2 Adults),1 queen bed,208.345,Canyamel,Spain,0,0,1,0,0,0,0,1
+Kn Hotel Arenas del Mar Adults Only,El Médano,7.6,8.0,Junior Suite with Mountain View (2 Adults),2 twin beds,382.714,El Médano,Spain,2,0,0,0,0,0,0,2
+Exe Boston,"Old Town, Zaragoza",8.0,8.4,Double Room,   1 double or 2 twins,218.15,Zaragoza,Spain,2,0,0,0,0,0,0,2
+Kn Hotel Matas Blancas - Solo Adultos,Costa Calma,7.5,8.1,Double Room (2 Adults),2 twin beds,247.87900000000002,Costa Calma,Spain,2,0,0,0,0,0,0,2
+Hotel Sierra de Cazorla & SPA 3*,Cazorla,8.3,8.4,Twin Room,2 twin beds,211.42000000000002,Cazorla,Spain,2,0,0,0,0,0,0,2
+El Escondite De Pedro Malillo,Candeleda,9.2,9.5,Two-Bedroom Townhouse,"5 beds (3 fulls, 2 sofa beds)",920.755,Candeleda,Spain,0,3,0,0,2,0,0,5
+SH Villa Gadea,Altea,8.9,9.3,Double Premium (2 Adults),   1 double or 2 twins,678.817,Altea,Spain,2,0,0,0,0,0,0,2
+Fonda Can Fasersia,La Pobla de Segur,8.0,0.0,Superior Double Room,1 full bed,128.975,La Pobla de Segur,Spain,0,1,0,0,0,0,0,1
+Hotel Ilunion Mérida Palace,Mérida,8.7,9.2,Double or Twin Room,   1 double or 2 twins,351.959,Mérida,Spain,2,0,0,0,0,0,0,2
+Barceló Teguise Beach - Adults Only,Costa Teguise,8.7,9.0,Deluxe Double or Twin Room with Spa Bath,1 queen bed,469.428,Costa Teguise,Spain,0,0,1,0,0,0,0,1
+Area Suites,Jove,8.6,9.2,Two-Bedroom Apartment,"4 beds (2 twins, 1 sofa bed, 1 queen)",167.093,Jove,Spain,2,0,1,0,0,0,0,4
+Apartamentos Cordial Magec Taurito,Taurito,8.9,9.2,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",184.26,Taurito,Spain,2,0,0,0,0,0,0,3
+Iberostar Málaga Playa,Torrox Costa,8.4,8.9,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",491.951,Torrox Costa,Spain,2,0,0,0,0,0,0,3
+La Palma - Teneguía Princess,Fuencaliente de la Palma,7.9,8.3,Double or Twin Room (2 Adults + 1 Child),"2 beds (1 sofa bed, 1 queen)",308.617,Fuencaliente de la Palma,Spain,0,0,1,0,0,0,0,2
+Hotel Miera,Liérganes,8.9,9.0,Standard Double Room,1 full bed,147.349,Liérganes,Spain,0,1,0,0,0,0,0,1
+Ibersol Antemare - Adults Only,"Sitges Beachfront, Sitges",7.9,8.1,Standard Double or Twin Room,   1 double or 2 twins,301.975,Sitges,Spain,2,0,0,0,0,0,0,2
+Helios Mallorca Hotel & Apartments,Can Pastilla,8.9,9.1,Twin Room with Balcony,2 twin beds,321.771,Can Pastilla,Spain,2,0,0,0,0,0,0,2
+Hotel Porto Calpe,Calpe,8.6,8.9,Double or Twin Room with Front Sea View,   1 double or 2 twins,251.452,Calpe,Spain,2,0,0,0,0,0,0,2
+HSM Lago Park,Playa de Muro,8.3,8.5,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",156.514,Playa de Muro,Spain,2,0,0,0,0,0,0,3
+ALEGRIA Costa Ballena AquaFun Hotel,Costa Ballena,8.3,8.6,Double Room with Extra Bed (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",291.688,Costa Ballena,Spain,2,0,0,0,0,0,0,3
+Hotel Helios Costa Tropical,Almuñécar,9.3,9.5,Classic Double or Twin Room,   1 double or 2 twins,253.657,Almuñécar,Spain,2,0,0,0,0,0,0,2
+Quartiers Marbella Apartments,Estepona,8.9,9.0,Three-Bedroom Apartment,"5 beds (4 twins, 1 king)",551.176,Estepona,Spain,4,0,0,1,0,0,0,5
+Palm Mar Apartments Tenerife South,Palm-Mar,7.7,8.6,One-Bedroom Apartment with Balcony,"2 beds (1 sofa bed, 1 queen)",168.138,Palm-Mar,Spain,0,0,1,0,0,0,0,2
+Apartamentos Canfranc 3000,Canfranc-Estación,7.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",125.401,Canfranc-Estación,Spain,0,1,0,0,0,0,0,2
+Alua Golf Trinidad,Roquetas de Mar,7.4,8.0,Double Superior Sea View,"3 beds (2 twins, 1 sofa bed)",189.721,Roquetas de Mar,Spain,2,0,0,0,0,0,0,3
+Servatur Green Beach,La Playa de Arguineguín,7.9,8.0,Standard Apartment with Sea View (2 Adults),"3 beds (2 twins, 1 sofa bed)",147.212,Arguineguín,Spain,2,0,0,0,0,0,0,3
+"Holiday Inn Express San Sebastian de los Reyes, an IHG Hotel",San Sebastián de los Reyes,8.1,8.3,Standard Double Room,   1 double or 2 twins,252.61100000000002,San Sebastián de los Reyes,Spain,2,0,0,0,0,0,0,2
+Beverly Hills Heights - Excel Hotels & Resorts,Los Cristianos,8.7,8.6,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",234.683,Los Cristianos,Spain,0,1,0,0,0,0,0,2
+Pierre & Vacances Resort Bonavista de Bonmont,Montroig,8.3,8.5,One-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",154.109,Montroig,Spain,0,2,0,0,0,0,0,3
+Hotel Lancelot,Arrecife,8.5,9.0,Double or Twin Room with Sea View,   1 double or 2 twins,288.26300000000003,Arrecife,Spain,2,0,0,0,0,0,0,2
+Apartamentos Mar Comillas,Comillas,9.0,9.0,One-Bedroom Apartment (2 Adults + 2 Children),"2 beds (1 full, 1 sofa bed)",151.489,Comillas,Spain,0,1,0,0,0,0,0,2
+Apartamentos Playa Langosteira,Fisterra,9.1,9.0,Penthouse Apartment,"3 beds (2 sofa beds, 1 queen)",175.216,Fisterra,Spain,0,0,1,0,2,0,0,3
+NH Gran Hotel Casino de Extremadura,Badajoz,9.0,9.4,Superior Double or Twin Room,   1 double or 2 twins,312.25600000000003,Badajoz,Spain,2,0,0,0,0,0,0,2
+Exe Tartessos,Huelva,8.1,8.6,Double Room,2 twin beds,227.897,Huelva,Spain,2,0,0,0,0,0,0,2
+Reverence Life Hotel - Adults Only,Santa Ponsa,8.2,8.8,Premium Room with Side Sea View,Multiple bed types,434.677,Santa Ponsa,Spain,0,0,0,0,0,0,0,0
+Archybal Apartamentos Turísticos y Suites,Archena,9.2,9.4,One-Bedroom Suite,,171.71800000000002,Archena,Spain,0,0,0,0,0,0,0,0
+Ramada Resort by Wyndham Puerto de Mazarron,Puerto de Mazarrón,8.3,9.0,"Single Beds, Deluxe Room, Mountain view",2 twin beds,170.57500000000002,Puerto de Mazarrón,Spain,2,0,0,0,0,0,0,2
+Apartamentos Tesy,La Manga del Mar Menor,8.4,8.3,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",146.282,La Manga del Mar Menor,Spain,0,1,0,0,0,0,0,2
+Interpass Golf Playa Country Club,Islantilla,8.0,8.1,Two-Bedroom Apartment in pool area,"3 beds (2 twins, 1 full)",265.447,Islantilla,Spain,2,1,0,0,0,0,0,3
+Expoholidays - Apartamentos Puerto Almerimar,Almerimar,8.1,8.2,One-Bedroom Apartment with City View,"2 beds (1 full, 1 sofa bed)",184.644,Almerimar,Spain,0,1,0,0,0,0,0,2
+Hotel Ecolife Tenerife,San Miguel de Abona,8.9,9.0,Double Room,2 twin beds,151.75,San Miguel de Abona,Spain,2,0,0,0,0,0,0,2
+Nuevo Hotel Vista Alegre,Valdepeñas,7.6,8.2,Twin Room,2 twin beds,226.621,Valdepeñas,Spain,2,0,0,0,0,0,0,2
+Aparthotel Ona Palamós,Palamós,8.2,8.4,Apartment with Balcony,"3 beds (2 twins, 1 sofa bed)",160.178,Palamós,Spain,2,0,0,0,0,0,0,3
+The Bloom by Pillow,Girona,8.4,8.8,Twin Room,2 twin beds,301.813,Girona,Spain,2,0,0,0,0,0,0,2
+AZZ Valencia Congress Hotel & Spa,Paterna,8.0,8.5,Double or Twin Room,Multiple bed types,207.582,Paterna,Spain,0,0,0,0,0,0,0,0
+Casas Rurales Taramundi Verde,Taramundi,8.9,8.9,One-Bedroom House,1 full bed,196.683,Taramundi,Spain,0,1,0,0,0,0,0,1
+NH Collection Cáceres Palacio de Oquendo,Cáceres,8.6,9.0,Superior Double or Twin Room,   1 double or 2 twins,328.273,Cáceres,Spain,2,0,0,0,0,0,0,2
+Arguineguín Park By Servatur,La Playa de Arguineguín,9.1,9.5,Premium Two-Bedroom Holiday Home - Type A (2 Adults),"4 beds (3 twins, 1 sofa bed)",211.747,Arguineguín,Spain,3,0,0,0,0,0,0,4
+Finca la Crucita Haria Lanzarote By PVL,Haría,7.9,8.2,Three-Bedroom Villa,"5 beds (4 twins, 1 king)",275.008,Haría,Spain,4,0,0,1,0,0,0,5
+Silken Monumental Naranco,Oviedo,8.5,8.9,Twin Room,2 twin beds,172.013,Oviedo,Spain,2,0,0,0,0,0,0,2
+Hotel Riu Oliva Beach Resort - All Inclusive,Corralejo,7.9,8.0,Double Room - Annex,"3 beds (2 twins, 1 sofa bed)",294.707,Corralejo,Spain,2,0,0,0,0,0,0,3
+Hotel & Spa Ciudad de Binéfar,Binéfar,8.8,9.5,Standard Apartment,"3 beds (2 twins, 1 sofa bed)",398.356,Binéfar,Spain,2,0,0,0,0,0,0,3
+Els Arenys,Sant Joan de les Abadesses,8.5,8.4,Apartment with Mountain View,"5 beds (2 twins, 2 fulls, 1 sofa bed)",513.6560000000001,Sant Joan de les Abadesses,Spain,2,2,0,0,0,0,0,5
+Camping San Javier,San Javier,7.8,0.0,Two-Bedroom Bungalow,"3 beds (1 full, 2 bunk beds)",233.931,San Javier,Spain,0,1,0,0,0,2,0,3
+Hotel Plaza Las Matas,Las Rozas de Madrid,8.1,8.6,Twin Room,2 twin beds,238.63,Las Rozas de Madrid,Spain,2,0,0,0,0,0,0,2
+Aparthotel la Piramide,Costa de Antigua,7.1,0.0,One-Bedroom Apartment with Pool View,"3 beds (2 twins, 1 sofa bed)",161.524,Costa de Antigua,Spain,2,0,0,0,0,0,0,3
+Pierre & Vacances Torredembarra,Torredembarra,8.3,8.3,Two-Bedroom Apartment (2-6 Adults),"5 beds (4 twins, 1 sofa bed)",161.054,Torredembarra,Spain,4,0,0,0,0,0,0,5
+Hotel Gran Bilbao,Bilbao,8.7,9.2,Quadruple Room,Multiple bed types,383.619,Bilbao,Spain,0,0,0,0,0,0,0,0
+Silken Gran hotel Durango,Durango,8.4,8.8,Superior Double Room with View,   1 double or 2 twins,343.818,Durango,Spain,2,0,0,0,0,0,0,2
+Palas Pineda,La Pineda,7.8,8.1,Standard Room,   1 double or 2 twins,145.685,La Pineda,Spain,2,0,0,0,0,0,0,2
+Wyndham Residences Golf del Sur,San Miguel de Abona,8.6,8.9,One Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",261.408,San Miguel de Abona,Spain,0,0,1,0,0,0,0,2
+ALEGRIA Barranco,Playa de las Americas,7.8,8.1,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",205.924,Playa de las Americas,Spain,2,0,0,0,0,0,0,3
+Exe Casa de Los Linajes,Segovia,8.3,8.5,Double or Twin Room,   1 double or 2 twins,272.107,Segovia,Spain,2,0,0,0,0,0,0,2
+Exe Cuenca,Cuenca,8.5,8.9,Double or Twin Room,   1 double or 2 twins,200.744,Cuenca,Spain,2,0,0,0,0,0,0,2
+Hotel RH Ifach,Calpe,8.6,8.9,Double or Twin Room with Terrace,Multiple bed types,232.356,Calpe,Spain,0,0,0,0,0,0,0,0
+Bungalowpark Isábena,La Puebla de Roda,8.2,0.0,Bungalow (2 Adults),"4 beds (2 twins, 1 full, 1 sofa bed)",168.138,La Puebla de Roda,Spain,2,1,0,0,0,0,0,4
+Vigilia Park,Puerto de Santiago,7.8,8.1,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",222.763,Puerto de Santiago,Spain,0,1,0,0,0,0,0,2
+Eurostars Gran Hotel Lugo,Lugo,8.9,9.3,Twin Room,2 twin beds,195.291,Lugo,Spain,2,0,0,0,0,0,0,2
+Secrets Lanzarote Resort & Spa - Adults Only (+18),Puerto Calero,8.6,9.3,Double Room Ocean View,"3 beds (2 fulls, 1 queen)",833.263,Puerto Calero,Spain,0,2,1,0,0,0,0,3
+Castilla Termal Olmedo,Olmedo,8.9,9.4,Double or Twin Room with Spa Access,   1 double or 2 twins,454.401,Olmedo,Spain,2,0,0,0,0,0,0,2
+Pierre & Vacances Blanes Playa,Blanes,8.0,0.0,Studio with Sea View,1 sofa bed,152.786,Blanes,Spain,0,0,0,0,0,0,0,0
+Macdonald Doña Lola Resort,La Cala de Mijas,8.5,8.6,Classic One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",304.327,La Cala de Mijas,Spain,0,1,0,0,0,0,0,2
+Hotel Nereida,L'Estartit,8.0,8.2,Double or Twin Room with Pool View,   1 double or 2 twins,248.668,L'Estartit,Spain,2,0,0,0,0,0,0,2
+Hostal Rey Teodomiro,Orihuela,7.1,0.0,Double Room,2 twin beds,109.655,Orihuela,Spain,2,0,0,0,0,0,0,2
+Grupoandria Aparthotel Club Andria,Cala Santandria,8.0,0.0,One-Bedroom Apartment,2 twin beds,178.697,Cala Santandria,Spain,2,0,0,0,0,0,0,2
+Hotel Andia,Orcoyen,8.3,8.7,Deluxe Double Room - Attic,1 full bed,238.354,Orcoyen,Spain,0,1,0,0,0,0,0,1
+RH Bayren Hotel & Spa 4* Sup,Gandía,8.9,9.3,Twin Room with Partial Sea View and Terrace,2 twin beds,316.699,Gandía,Spain,2,0,0,0,0,0,0,2
+Fidalsa Famous Spot,Campoamor,8.1,0.0,Four-Bedroom House,"6 beds (4 twins, 2 fulls)",604.764,Campoamor,Spain,4,2,0,0,0,0,0,6
+Apartamento Malpica Area Grande,Malpica,9.6,9.9,Apartment with Sea View,"5 beds (4 twins, 1 full)",315.807,Malpica,Spain,4,1,0,0,0,0,0,5
+Occidental Jandía Mar,Morro del Jable,7.9,8.3,Double or Twin Room with Sea View (2 Adults + 1 Child),"2 beds (1 twin, 1 king)",266.653,Morro del Jable,Spain,1,0,0,1,0,0,0,2
+LA POSADA DE HIGUERUELA,Higueruela,8.4,8.4,Double,2 twin beds,171.474,Higueruela,Spain,2,0,0,0,0,0,0,2
+Hotel Planamar by Escampa Hotels,Platja d'Aro,8.3,8.7,   Quadruple Room,Multiple bed types,388.84000000000003,Platja d'Aro,Spain,0,0,0,0,0,0,0,0
+Hostal Sa Baronia,Banyalbufar,8.1,8.2,Double Room with Balcony and Sea View,2 twin beds,316.781,Banyalbufar,Spain,2,0,0,0,0,0,0,2
+"Holiday Inn Express Vitoria, an IHG Hotel",Vitoria-Gasteiz,8.2,8.5,Double or Twin Room (1-2 Adults) with Free Parking,   1 double or 2 twins,197.275,Vitoria-Gasteiz,Spain,2,0,0,0,0,0,0,2
+Hotel Montecarlo Spa & Wellness,"Santa Margarida, Roses",8.1,8.4,Premium Double or Twin Room with Lateral Sea View and Spa Access,"3 beds (2 twins, 1 queen)",302.857,Roses,Spain,2,0,1,0,0,0,0,3
+Coral Cotillo Beach,El Cotillo,8.6,9.0,Double Room with Pool View (2 Adults),2 twin beds,208.171,El Cotillo,Spain,2,0,0,0,0,0,0,2
+Allegro Isora,Puerto de Santiago,7.9,8.3,Apartment with Pool View (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",313.387,Puerto de Santiago,Spain,2,0,0,0,0,0,0,3
+Complejo Bubal Formigal 3000,Búbal,7.2,0.0,One-Bedroom Apartment,"3 beds (2 bunk beds, 1 sofa bed)",126.479,Búbal,Spain,0,0,0,0,0,2,0,3
+Elba Estepona Gran Hotel & Thalasso Spa,Estepona,8.2,8.7,Deluxe Double Room with Garden View (2 Adults),   1 double or 2 twins,438.04,Estepona,Spain,2,0,0,0,0,0,0,2
+Hotel Ilusion Moreyo - Adults Only,Cala Bona,8.0,8.3,Twin Room with City View,2 twin beds,247.638,Son Servera,Espagne,2,0,0,0,0,0,0,2
+Helios Benidorm,Benidorm,9.1,9.5,Twin Room with Pool View,2 twin beds,302.741,Benidorm,Spain,2,0,0,0,0,0,0,2
+Cortijo Lagar de Luisa,Borge,9.5,9.5,One-Bedroom House,2 twin beds,212.0,Borge,Spain,2,0,0,0,0,0,0,2
+Apartahotel Playa Conil,"City Center, Conil de la Frontera",8.1,8.0,Two-Bedroom Apartment,Multiple bed types,240.197,Conil de la Frontera,Spain,0,0,0,0,0,0,0,0
+Hospedium Hotel Abril,San Juan de Alicante,8.1,8.3,Superior Double Room,1 queen bed,202.717,San Juan de Alicante,Spain,0,0,1,0,0,0,0,1
+Catalonia del Mar - Adults Only,Cala Bona,8.2,8.5,Superior Double or Twin Room with Mountain View,   1 double or 2 twins,200.512,Son Servera,Espagne,2,0,0,0,0,0,0,2
+Hospedium Hotel Continental,Mojácar,8.7,9.0,Double Room with City View,1 full bed,187.748,Mojácar,Spain,0,1,0,0,0,0,0,1
+Apartamentos Rurales Altuzarra,Ezcaray,8.4,8.1,Two-Bedroom Apartment with Balcony,"6 beds (4 twins, 2 sofa beds)",254.702,Ezcaray,Spain,4,0,0,0,2,0,0,6
+Ilunion Sancti Petri,Chiclana de la Frontera,8.4,8.6,One-Bedroom Apartment (2 Adults),2 twin beds,215.115,Chiclana de la Frontera,Spain,2,0,0,0,0,0,0,2
+Hotel Iriguibel Huarte Pamplona,Huarte,8.4,8.8,Twin Room,2 twin beds,189.651,Huarte,Spain,2,0,0,0,0,0,0,2
+Hostal Kavanna,San Pedro de Mérida,8.7,9.0,Double Room,1 full bed,140.405,San Pedro de Mérida,Spain,0,1,0,0,0,0,0,1
+Maristel Hotel & Spa,Estellencs,8.5,8.9,Superior Double or Twin Room with Sea View,2 twin beds,380.625,Estellencs,Spain,2,0,0,0,0,0,0,2
+ON ALETA ROOM designed for adults,Almuñécar,9.2,9.5,Double Room with Sea View,1 king bed,279.151,Almuñécar,Spain,0,0,0,1,0,0,0,1
+Lemon & Soul Hotel Cactus Garden,Playa Jandia,8.2,8.6,Deluxe Junior Suite (2 Adults),"3 beds (2 twins, 1 sofa bed)",430.034,Playa Jandia,Spain,2,0,0,0,0,0,0,3
+Molino de Guatiza,Costa Teguise,7.8,0.0,Two-Bedroom Apartment with Sea View - Second Floor,"5 beds (4 twins, 1 sofa bed)",207.242,Costa Teguise,Spain,4,0,0,0,0,0,0,5
+Abora Continental by Lopesan Hotels,Playa del Ingles,8.2,8.8,Double Standard (2 adults),2 twin beds,423.382,Playa del Ingles,Spain,2,0,0,0,0,0,0,2
+Hotel RH Riviera - Adults Only,Gandía,8.5,8.7,Double or Twin Room with Terrace,   1 double or 2 twins,204.267,Gandía,Spain,2,0,0,0,0,0,0,2
+Ramada Residences by Wyndham Costa Adeje,Adeje,8.5,8.8,One-Bedroom Apartment with Ocean View,"2 beds (1 full, 1 sofa bed)",224.648,Adeje,Spain,0,1,0,0,0,0,0,2
+Hotel Villa de Verín,Verín,8.5,8.9,Twin Room,2 full beds,123.173,Verín,Spain,0,2,0,0,0,0,0,2
+Hotel & Spa María Manuela,Onís,8.0,8.4,Double or Twin Room,   1 double or 2 twins,298.738,Onís,Spain,2,0,0,0,0,0,0,2
+Eurostars Zaragoza,Zaragoza,8.0,8.5,Double or Twin Room,   1 double or 2 twins,202.949,Zaragoza,Spain,2,0,0,0,0,0,0,2
+Balneario de Ledesma,Vega de Tirados,7.0,0.0,Twin Room,   1 double or 2 twins,180.35,Vega de Tirados,Spain,2,0,0,0,0,0,0,2
+Blue House Mallorca,Ses Salines,9.6,9.7,One-Bedroom Apartment,1 queen bed,392.565,Ses Salines,Spain,0,0,1,0,0,0,0,1
+Hotel Marina,Palamós,8.3,8.5,Double Room with Balcony,1 queen bed,208.031,Palamós,Spain,0,0,1,0,0,0,0,1
+Hotel Apartamentos Manilva Sun,Manilva,8.1,8.3,One-Bedroom Apartment,1 full bed,158.33,Manilva,Spain,0,1,0,0,0,0,0,1
+Apartamentos Ardales,Ardales,9.1,9.3,Duplex Apartment (3 Adults),"2 beds (1 twin, 1 full)",250.988,Ardales,Spain,1,1,0,0,0,0,0,2
+Hostal Marina Cadaqués,Cadaqués,8.4,8.7,Deluxe Double Room with Balcony and Sea View,1 queen bed,432.43600000000004,Cadaqués,Spain,0,0,1,0,0,0,0,1
+Catalonia Ronda,Ronda,9.2,9.7,Superior Double Room,Multiple bed types,498.96000000000004,Ronda,Spain,0,0,0,0,0,0,0,0
+Can Torrents,Cunit,8.3,8.1,Standard Apartment,"3 beds (2 twins, 1 sofa bed)",146.207,Cunit,Spain,2,0,0,0,0,0,0,3
+Prinsotel La Dorada - 4* Sup,Playa de Muro,8.6,8.7,One Bedroom Suite with Balcony,"2 beds (1 sofa bed, 1 queen)",724.82,Playa de Muro,Spain,0,0,1,0,0,0,0,2
+Hotel Águila Real,Cangas de Onís,7.2,0.0,Double or Twin Room with Extra Bed,Multiple bed types,163.844,Cangas de Onís,Spain,0,0,0,0,0,0,0,0
+Lago Garden Apart-Suites & Spa Hotel,Cala Ratjada,8.6,9.0,Double Standard,3 twin beds,466.80400000000003,Cala Ratjada,Spain,3,0,0,0,0,0,0,3
+R2 Bahia Playa - Adults Only,Tarajalejo,8.2,8.7,Double Room,2 twin beds,355.228,Tarajalejo,Spain,2,0,0,0,0,0,0,2
+Salobre Hotel Resort & Serenity,Salobre,9.1,9.5,Deluxe Room (2 Adults + 1 Child),Multiple bed types,625.44,Salobre,Spain,0,0,0,0,0,0,0,0
+Diana Park,Estepona,8.1,8.2,Twin Room,2 twin beds,162.249,Estepona,Spain,2,0,0,0,0,0,0,2
+AC Hotel Elda by Marriott,Elda,8.4,8.9,"Standard Twin, Guest room, 2 Twin/Single Bed(s)",2 twin beds,219.542,Elda,Spain,2,0,0,0,0,0,0,2
+Village Golf Beach,Pals,8.4,8.3,Two-Bedroom Villa,"3 beds (2 twins, 1 queen)",304.598,Pals,Spain,2,0,1,0,0,0,0,3
+Hotel Don Juan Tossa,Tossa de Mar,8.3,8.5,Superior Double Room,2 twin beds,118.354,Tossa de Mar,Spain,2,0,0,0,0,0,0,2
+Valle del Nilo,Ventas de Poyo,7.5,0.0,Superior Suite,1 king bed,551.7570000000001,Ventas de Poyo,Spain,0,0,0,1,0,0,0,1
+Playa de Camariñas,Camariñas,8.6,8.7,Triple Room with Courtyard View,3 twin beds,185.195,Camariñas,Spain,3,0,0,0,0,0,0,3
+Hostal La Palma,Fornells,8.6,8.9,Standard Twin Room with Garden View,2 twin beds,267.82,Fornells,Spain,2,0,0,0,0,0,0,2
+Hotel Bahia,Puerto de Mazarrón,7.1,0.0,Double or Twin Room,   1 double or 2 twins,146.207,Puerto de Mazarrón,Spain,2,0,0,0,0,0,0,2
+Bed&Breakfast 10 GIRONA,Girona,7.3,0.0,Double or Twin Room with Private Bathroom,1 full bed,199.46800000000002,Girona,Spain,0,1,0,0,0,0,0,1
+Elba Sunset Mallorca Thalasso Spa,Palmanova,8.7,9.1,Junior Suite Nereida - Sea View (2 Adults),"2 beds (1 sofa bed, 1 queen)",802.453,Palmanova,Spain,0,0,1,0,0,0,0,2
+Villa Verano Villaverde,La Oliva,8.8,8.2,Double Room with Private Bathroom,2 twin beds,85.287,La Oliva,Spain,2,0,0,0,0,0,0,2
+Exe Alfonso VIII,Plasencia,8.6,9.0,Double Room - 1 o 2 Beds,2 twin beds,252.613,Plasencia,Spain,2,0,0,0,0,0,0,2
+Corallium Beach by Lopesan Hotels - Adults Only,San Agustin,8.7,9.1,Double with Side Sea View,1 queen bed,342.124,San Agustin,Spain,0,0,1,0,0,0,0,1
+Hermida Rural,Eirejalba,9.3,9.3,Double Room,1 full bed,157.642,Eirejalba,Spain,0,1,0,0,0,0,0,1
+Barceló Punta Umbría Mar,Punta Umbría,8.5,8.8,Deluxe Double Room with Balcony and Sea View,2 twin beds,359.837,Punta Umbría,Spain,2,0,0,0,0,0,0,2
+Hotel Spa Mediterraneo Park,"Santa Margarida, Roses",8.8,9.2,Twin Room,   1 double or 2 twins,245.627,Roses,Spain,2,0,0,0,0,0,0,2
+Hotel Villa Bandama Golf - Adults Only,Santa Brígida,8.9,9.1,Superior Double Room,1 queen bed,197.228,Santa Brígida,Spain,0,0,1,0,0,0,0,1
+Gran Melia Palacio de Isora Resort & Spa,Alcalá,8.4,9.0,One-Bedroom Master Suite with Resort View,1 king bed,1123.3310000000001,Alcalá,Spain,0,0,0,1,0,0,0,1
+Shambhala Fuerteventura,Parque Holandes,9.3,9.5,Apartment with Pool View,"2 beds (1 full, 1 sofa bed)",293.226,Parque Holandes,Spain,0,1,0,0,0,0,0,2
+Hotel Windsor,Tossa de Mar,9.1,9.3,Superior Double or Twin Room with Terrace,   1 double or 2 twins,198.737,Tossa de Mar,Spain,2,0,0,0,0,0,0,2
+MaraVillas de Tenerife,Abades,9.1,9.3,Basement Apartment,"3 beds (2 twins, 1 full)",155.954,Abades,Spain,2,1,0,0,0,0,0,3
+Paloma Beach Apartments,Los Cristianos,8.4,8.5,Superior Apartment,2 twin beds,214.437,Los Cristianos,Spain,2,0,0,0,0,0,0,2
+Ona Garden Lago,Port d'Alcudia,8.5,8.6,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",262.42,Port d'Alcudia,Spain,2,1,0,0,0,0,0,4
+Salles Hotels Marina Portals,Portals Nous,8.0,8.5,Cycling Package - Deluxe Room with Spa Access,2 twin beds,382.226,Portals Nous,Spain,2,0,0,0,0,0,0,2
+INNSiDE by Meliá Calviá Beach,Magaluf,8.5,8.9,The Superior Innside Room,2 twin beds,343.238,Magaluf,Spain,2,0,0,0,0,0,0,2
+AC Hotel La Línea by Marriott,La Línea de la Concepción,7.7,8.2,Twin Room with City View,1 full bed,181.735,La Línea de la Concepción,Spain,0,1,0,0,0,0,0,1
+Beverly Hills Suites - Excel Hotels & Resorts,Los Cristianos,7.9,0.0,Studio,"3 beds (2 twins, 1 sofa bed)",193.244,Los Cristianos,Spain,2,0,0,0,0,0,0,3
+Pins Platja Apartments,Cambrils,7.8,0.0,Standard One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",173.255,Cambrils,Spain,2,0,0,0,0,0,0,3
+Apartamentos PortoDrach,Porto Cristo,9.0,9.2,One-Bedroom Apartment with Balcony and Harbor View,"3 beds (2 twins, 1 sofa bed)",320.402,Porto Cristo,Spain,2,0,0,0,0,0,0,3
+Pensión Felipe,Carboneras,7.4,0.0,Double Room,1 full bed,125.32000000000001,Carboneras,Spain,0,1,0,0,0,0,0,1
+Casa-Palo-Pique,Icod de los Vinos,8.6,8.5,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",107.194,Icod de los Vinos,Spain,0,0,1,0,0,0,0,2
+O Remanso Dos Patos,Penalba,8.7,8.7,Family Junior Suite,"3 beds (2 sofa beds, 1 queen)",363.08,Penalba,Spain,0,0,1,0,2,0,0,3
+Hotel Terraverda,Torroella de Montgrí,8.9,9.4,Superior Double or Twin Room with Golf View and Terrace,Multiple bed types,502.971,Torroella de Montgrí,Spain,0,0,0,0,0,0,0,0
+Mesonhomes Suites Center - Stay & Work,"Granada City Center, Granada",8.1,8.1,Studio Apartment,"3 beds (1 full, 2 bunk beds)",270.169,Granada City Center,Spain,0,1,0,0,0,2,0,3
+Apartamentos Tamaragua,Playa del Ingles,8.3,8.2,Two-Bedroom Apartment,"5 beds (4 twins, 1 sofa bed)",164.413,Playa del Ingles,Spain,4,0,0,0,0,0,0,5
+Turbo Club,Maspalomas,8.0,8.5,Superior Apartment (2 Adults),2 twin beds,313.997,Maspalomas,Spain,2,0,0,0,0,0,0,2
+Casa de Aldea El Frade,Camango,9.3,9.4,Double or Twin Room,1 full bed,158.147,Camango,Spain,0,1,0,0,0,0,0,1
+Delamar 4*Sup-Adults only (18+),"Lloret Town Center, Lloret de Mar",9.2,9.4,Double or Twin Room,   1 double or 2 twins,218.498,Lloret de Mar,Spain,2,0,0,0,0,0,0,2
+Globales Reina Cristina,Algeciras,7.4,0.0,Double or Twin Room,   1 double or 2 twins,192.15800000000002,Algeciras,Spain,2,0,0,0,0,0,0,2
+Hotel Alhambra,"Santa Susanna Beach, Santa Susanna",7.8,8.1,Double or Twin Room,   1 double or 2 twins,172.199,Santa Susanna Beach,Spain,2,0,0,0,0,0,0,2
+Letmalaga Hotel Albaicín,Coín,8.2,8.7,Standard Double Room,1 full bed,161.872,Coín,Spain,0,1,0,0,0,0,0,1
+Salles Hotel Aeroport de Girona,Riudellots de la Selva,8.1,8.4,Double or Twin Room with Spa Access,   1 double or 2 twins,223.604,Riudellots de la Selva,Spain,2,0,0,0,0,0,0,2
+Hotel Bulevar Burgos,Burgos,8.0,8.2,Twin Room,2 twin beds,174.717,Burgos,Spain,2,0,0,0,0,0,0,2
+La Villa,El Paso,9.0,9.1,One-Bedroom Apartment,2 twin beds,196.161,El Paso,Spain,2,0,0,0,0,0,0,2
+Hotel Faranda Marsol Candás,Candás,7.8,8.1,Double or Twin Room,   1 double or 2 twins,206.964,Candás,Spain,2,0,0,0,0,0,0,2
+Bahia Principe Sunlight Costa Adeje,Adeje,8.4,8.7,Double or Twin Room with Side Sea View,Multiple bed types,509.229,Adeje,Spain,0,0,0,0,0,0,0,0
+Hipotels Hipocampo Playa,Cala Millor,8.8,9.0,Double Room with Pool View,Multiple bed types,320.652,Cala Millor,Spain,0,0,0,0,0,0,0,0
+Royal Sun Resort,Acantilado de los Gigantes,8.4,8.8,Two-Bedroom Apartment (6 Adults),"4 beds (2 twins, 1 sofa bed, 1 queen)",449.586,Acantilado de los Gigantes,Spain,2,0,1,0,0,0,0,4
+Apartamentos Anjomacar II,Teguise,8.7,8.9,Apartment,"2 beds (1 sofa bed, 1 queen)",195.291,Teguise,Spain,0,0,1,0,0,0,0,2
+ALEGRIA Cabo de Gata,Retamar,7.7,8.0,Standard Double Room,2 full beds,162.684,Retamar,Spain,0,2,0,0,0,0,0,2
+Hostal Ca'n Pep,Sa Ràpita,7.8,8.2,Double Room with Balcony,2 twin beds,197.727,Sa Ràpita,Spain,2,0,0,0,0,0,0,2
+BULL Reina Isabel & SPA,Las Palmas de Gran Canaria,8.4,8.8,Twin Room Spa Acces included,2 twin beds,297.444,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Habitat Cm Muxia,Muxia,8.4,8.6,Twin Room,2 twin beds,166.67600000000002,Muxia,Spain,2,0,0,0,0,0,0,2
+Cura Sol,Playa del Cura,8.4,8.2,One-Bedroom Apartment (2 Adults),1 queen bed,174.111,Playa del Cura,Spain,0,0,1,0,0,0,0,1
+Bahia Blanca,Puerto Rico de Gran Canaria,8.7,8.8,Superior One-Bedroom Apartment with Sea View (2-4 Adults),"2 beds (1 sofa bed, 1 queen)",337.083,Puerto Rico de Gran Canaria,Spain,0,0,1,0,0,0,0,2
+Apartamentos Mar y Sol,Miami Platja,8.7,8.6,Standard One-Bedroom Apartment,1 full bed,316.781,Miami Platja,Spain,0,1,0,0,0,0,0,1
+Menorca Experimental,Alaior,9.0,9.4,Superior Double Room - Alaior Terrace,1 king bed,830.4780000000001,Alaior,Spain,0,0,0,1,0,0,0,1
+Hoposa Niu,Cala de Sant Vicenc,8.7,9.0,Double or Twin Room with Sea View,   1 double or 2 twins,455.504,Cala de Sant Vicenc,Spain,2,0,0,0,0,0,0,2
+Apartamentos Cocoteros - Tamara,Maspalomas,8.1,8.4,One-Bedroom Apartment Deluxe - Tamara,2 twin beds,331.946,Maspalomas,Spain,2,0,0,0,0,0,0,2
+Hotel Playa de Laxe,"Playa de Laxe, Laxe",8.9,9.2,Family Room,Multiple bed types,197.147,Playa de Laxe,Spain,0,0,0,0,0,0,0,0
+Posada y Apartamentos Trebuesto de Guriezo,Trebuesto,8.5,9.1,Double Room with Mountain View,1 queen bed,282.666,Trebuesto,Spain,0,0,1,0,0,0,0,1
+AC Hotel Algeciras by Marriott,Algeciras,8.4,8.9,"Standard Room Twin, Guest room, 2 Twin/Single Bed(s)",2 twin beds,188.27,Algeciras,Spain,2,0,0,0,0,0,0,2
+Hotel Castillo de Monda,Monda,8.8,9.0,Deluxe Room,1 full bed,570.439,Monda,Spain,0,1,0,0,0,0,0,1
+YIT Conquista de Granada,Peligros,8.1,8.4,Twin Room,2 twin beds,199.12,Peligros,Spain,2,0,0,0,0,0,0,2
+Barceló Conil Playa - Adults Recommended,"Fuente del Gallo Beach, Conil de la Frontera",8.8,9.4,Double or Twin Room with Side Sea View,Multiple bed types,386.169,Fuente del Gallo Beach,Spain,0,0,0,0,0,0,0,0
+Pensión - Restaurante VILABOA,Bóveda,8.3,8.3,Queen Room,1 full bed,146.207,Bóveda,Spain,0,1,0,0,0,0,0,1
+Residencial Super Stop Palafrugell,Palafrugell,7.9,0.0,Superior One-Bedroom Apartment (4 Adults),"3 beds (2 twins, 1 sofa bed)",146.207,Palafrugell,Spain,2,0,0,0,0,0,0,3
+Hotel Rosamar,Sant Antoni de Calonge,8.2,8.6,Double or Twin Room with Pool View or Natural Solarium View,2 twin beds,195.987,Sant Antoni de Calonge,Spain,2,0,0,0,0,0,0,2
+Blue Sardine Hostal Boutique Altea Adults Only,Altea,8.8,9.3,Apartment - Ground Floor,1 queen bed,488.40000000000003,Altea,Spain,0,0,1,0,0,0,0,1
+Hotel Oria,Tolosa,7.9,8.3,Twin Room,2 twin beds,207.59,Tolosa,Spain,2,0,0,0,0,0,0,2
+AZZ Valencia Táctica Hotel,Paterna,7.8,8.1,Double or Twin Room,Multiple bed types,211.833,Paterna,Spain,0,0,0,0,0,0,0,0
+Hotel U Viveiro,Viveiro,9.1,9.6,Junior Suite,1 king bed,346.951,Viveiro,Spain,0,0,0,1,0,0,0,1
+Gran Hotel Luna de Granada,"Ronda District, Granada",8.5,8.9,Double Room,2 twin beds,306.454,Ronda District,Spain,2,0,0,0,0,0,0,2
+Soho Boutique Oviedo,"City Centre, Oviedo",8.6,8.9,Double or Twin Room,   1 double or 2 twins,211.072,Oviedo,Spain,2,0,0,0,0,0,0,2
+Hotel Club Sunway Punta Prima,Es Pujols,8.4,8.5,Double Room,   1 double or 2 twins,321.539,Es Pujols,Spain,2,0,0,0,0,0,0,2
+TRH Paraíso,Estepona,7.7,8.0,Double or Twin Room with Mountain View,   1 double or 2 twins,207.892,Estepona,Spain,2,0,0,0,0,0,0,2
+NH Castellón Turcosa,Grao de Castellón,8.4,8.8,Standard Double or Twin Room,   1 double or 2 twins,194.249,Grao de Castellón,Spain,2,0,0,0,0,0,0,2
+Tacande Portals,Portals Nous,7.6,8.3,Deluxe Room located at the Cala area,   1 double or 2 twins,344.421,Portals Nous,Spain,2,0,0,0,0,0,0,2
+Hotel Arce,San Pedro del Pinatar,8.1,8.0,Budget Double or Twin Room,   1 double or 2 twins,105.399,San Pedro del Pinatar,Spain,2,0,0,0,0,0,0,2
+Leonardo Hotel Fuengirola Costa del Sol,"Fuengirola City Centre, Fuengirola",8.1,8.3,Superior Twin Room with Sea View,   1 double or 2 twins,292.298,Fuengirola City Centre,Spain,2,0,0,0,0,0,0,2
+Hotel Creu de Tau Art&Spa-Adults only,Capdepera,9.6,9.8,Suite with Terrace,2 twin beds,1508.611,Capdepera,Spain,2,0,0,0,0,0,0,2
+Hotel Pamplona Cross Elorz,Imárcoain,7.7,0.0,Double Room,1 full bed,154.329,Imárcoain,Spain,0,1,0,0,0,0,0,1
+Apartamentos Turísticos Playa de Osmo,Corme-Puerto,9.0,9.2,Apartment with Side Sea View,"3 beds (2 twins, 1 sofa bed)",176.376,Corme-Puerto,Spain,2,0,0,0,0,0,0,3
+Chateau La Roca,Sancibrián,8.4,8.8,Standard Twin Room,2 twin beds,169.066,Sancibrián,Spain,2,0,0,0,0,0,0,2
+Albir Hills Apartments,Albir,8.8,8.6,Superior Apartment,1 full bed,215.249,Albir,Spain,0,1,0,0,0,0,0,1
+Hotel Alda Ciudad de Toro,Toro,7.9,8.0,Standard Double or Twin Room,   1 double or 2 twins,141.65200000000002,Toro,Spain,2,0,0,0,0,0,0,2
+HOTEL JAUME D'URGELL,Balaguer,7.7,8.1,Twin Room,2 twin beds,172.315,Balaguer,Spain,2,0,0,0,0,0,0,2
+Hostal Fuente del Pino,Jumilla,7.6,8.1,Double Room,1 full bed,97.471,Jumilla,Spain,0,1,0,0,0,0,0,1
+Apartamentos Tarahal,San Agustin,8.5,8.6,One-Bedroom Apartment (2 Adults + 2 Children),"3 beds (2 twins, 1 sofa bed)",190.8,San Agustin,Spain,2,0,0,0,0,0,0,3
+Itaca Sevilla by Soho Boutique,"Old Town, Seville",7.9,8.6,Double or Twin Room,   1 double or 2 twins,641.569,Seville,Spain,2,0,0,0,0,0,0,2
+Barceló Tenerife,San Miguel de Abona,8.3,8.8,Double Room,Multiple bed types,431.963,San Miguel de Abona,Spain,0,0,0,0,0,0,0,0
+Castilla Termal Solares,Solares,8.8,9.3,Double or Twin Room with Spa Access,2 twin beds,387.68,Solares,Spain,2,0,0,0,0,0,0,2
+Monsuau Cala D'Or Hotel 4 Sup - Adults Only,Cala d´Or,8.7,9.2,Premium Room - Swim Up,Multiple bed types,562.251,Santanyí,Espagne,0,0,0,0,0,0,0,0
+Hard Rock Hotel Tenerife,Adeje,8.7,9.2,Studio Suite Gold,"3 beds (2 twins, 1 sofa bed)",630.105,Adeje,Spain,2,0,0,0,0,0,0,3
+La Casa Del Miracle,Balaguer,9.8,9.9,Apartment,"6 beds (4 fulls, 2 sofa beds)",515.988,Balaguer,Spain,0,4,0,0,2,0,0,6
+Camping Vendrell Platja,Comarruga,8.4,8.2,Tent Grand Safari,"4 beds (1 twin, 1 full, 2 bunk beds)",207.47400000000002,Comarruga,Spain,1,1,0,0,0,2,0,4
+AC Hotel Badajoz by Marriott,Badajoz,8.1,8.5,Double Room,1 queen bed,190.955,Badajoz,Spain,0,0,1,0,0,0,0,1
+Hotel Reimar,Sant Antoni de Calonge,8.3,8.5,Standard Double Room with Balcony,   1 double or 2 twins,189.552,Sant Antoni de Calonge,Spain,2,0,0,0,0,0,0,2
+Hotel-Restaurante Venta Tomas,Almuradiel,7.7,8.0,Double Room,1 full bed,134.023,Almuradiel,Spain,0,1,0,0,0,0,0,1
+Casas de Benaojan 15,Benaoján,9.6,9.6,Holiday Home,"7 beds (4 twins, 1 full, 1 bunk bed, 1 sofa bed)",341.149,Benaoján,Spain,4,1,0,0,0,0,0,7
+Residencial El Llano,Valle Gran Rey,8.7,8.7,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",204.225,Valle Gran Rey,Spain,2,0,0,0,0,0,0,3
+Apartamentos Hesperia Bristol Playa,Corralejo,6.9,0.0,One-Bedroom Apartment with Sea View,2 twin beds,216.757,Corralejo,Spain,2,0,0,0,0,0,0,2
+Vacances Menorca Blanc Palace,Sa Caleta,7.1,0.0,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",128.403,Sa Caleta,Spain,2,0,0,0,0,0,0,3
+Red Level at Gran Melia Palacio de Isora - Adults Only,Alcalá,8.9,9.4,Deluxe Double Room with Side Ocean View and Hot Tub,1 king bed,959.8480000000001,Alcalá,Spain,0,0,0,1,0,0,0,1
+Hotel Roses Platja,"Santa Margarida, Roses",7.8,8.1,Standard Double Room,   1 double or 2 twins,208.762,Roses,Spain,2,0,0,0,0,0,0,2
+Alda Mirador del Moncayo,Olvega,8.4,9.1,Triple Room,3 twin beds,229.179,Olvega,Spain,3,0,0,0,0,0,0,3
+TRH La Motilla,Dos Hermanas,8.3,8.6,Twin Room,2 twin beds,281.692,Dos Hermanas,Spain,2,0,0,0,0,0,0,2
+Vincci Tenerife Golf,San Miguel de Abona,7.7,8.2,Double Room,2 twin beds,251.80100000000002,San Miguel de Abona,Spain,2,0,0,0,0,0,0,2
+Be Free Isla Plana,Isla Plana,7.4,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",132.526,Isla Plana,Spain,0,1,0,0,0,0,0,2
+Hostal La Cuna del Sella,Oseja de Sajambre,9.3,9.5,Basic Twin Room,2 twin beds,232.42600000000002,Oseja de Sajambre,Spain,2,0,0,0,0,0,0,2
+Mercure Algeciras,Algeciras,7.9,8.2,Superior Room,1 queen bed,169.41400000000002,Algeciras,Spain,0,0,1,0,0,0,0,1
+OCEAN HOMES Apartamentos exclusivos en Isla Canela,Isla Canela,9.5,9.4,Apartment - Ground Floor,"3 beds (2 twins, 1 full)",363.115,Isla Canela,Spain,2,1,0,0,0,0,0,3
+La Vega de Pendueles,Pendueles,8.7,8.9,Superior Double Room,1 full bed,222.095,Pendueles,Spain,0,1,0,0,0,0,0,1
+Apartamentos Boutique Granada 3000,"Albaicin, Granada",8.4,8.6,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",305.252,Granada,Spain,0,1,0,0,0,0,0,2
+Carema Garden Village,Fornells,7.4,0.0,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",134.18800000000002,Fornells,Spain,2,0,0,0,0,0,0,3
+Sol Fuerteventura Jandia - All Suites,Morro del Jable,8.2,8.8,Junior Suite with Terrace,"3 beds (2 twins, 1 sofa bed)",401.372,Morro del Jable,Spain,2,0,0,0,0,0,0,3
+Rentalmar Pins Marina,Cambrils,7.1,0.0,One-Bedroom Apartment (2 Adults),2 twin beds,100.488,Cambrils,Spain,2,0,0,0,0,0,0,2
+TRH Jardín Del Mar,Santa Ponsa,7.7,0.0,One-Bedroom Apartment with Sea View (3 Adults),"3 beds (2 twins, 1 sofa bed)",194.565,Santa Ponsa,Spain,2,0,0,0,0,0,0,3
+"INNER Hotel Rupit ""Adults Only""",Cala d´Or,9.1,9.0,Double Room with Pool View,   1 double or 2 twins,274.706,Santanyí,Espagne,2,0,0,0,0,0,0,2
+Canyamel Park Hotel & Spa - 4* Sup - Adults only (+16),Canyamel,8.7,9.2,Double Room Plus,2 twin beds,265.122,Canyamel,Spain,2,0,0,0,0,0,0,2
+Sunset Bay Club,Adeje,8.4,8.7,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",343.354,Adeje,Spain,0,1,0,0,0,0,0,2
+Puerta del Sol,Ciudad-Rodrigo,8.3,8.4,Quadruple Room,Multiple bed types,299.37600000000003,Ciudad-Rodrigo,Spain,0,0,0,0,0,0,0,0
+INNSiDE by Meliá Alcudia,Port d'Alcudia,8.5,9.0,The Loft Lagoon View,"3 beds (2 twins, 1 sofa bed)",378.281,Port d'Alcudia,Spain,2,0,0,0,0,0,0,3
+Hotel Bemon Playa,Somo,8.2,8.3,Double or Twin Room,2 twin beds,182.341,Somo,Spain,2,0,0,0,0,0,0,2
+Hotel Jucamar,Cangas de Morrazo,8.1,8.4,Superior Double Room,Multiple bed types,131.47,Cangas de Morrazo,Spain,0,0,0,0,0,0,0,0
+Apartamentos Balcon del Cielo,Trevélez,9.5,9.4,Apartment,1 full bed,171.851,Trevélez,Spain,0,1,0,0,0,0,0,1
+Ilunion Caleta Park,Sant Feliu de Guíxols,8.1,8.5,Double or Twin Room,   1 double or 2 twins,210.578,Sant Feliu de Guíxols,Spain,2,0,0,0,0,0,0,2
+Hipotels Bahia Cala Millor - Adults Only,Cala Millor,9.1,9.4,Double Room with Pool View,3 twin beds,287.02500000000003,Cala Millor,Spain,3,0,0,0,0,0,0,3
+Rodeiramar 2A,Cangas de Morrazo,9.4,9.5,Duplex Apartment,"2 beds (1 sofa bed, 1 queen)",195.116,Cangas de Morrazo,Spain,0,0,1,0,0,0,0,2
+Camping Del Mar,"Malgrat de Mar Beach, Malgrat de Mar",8.2,8.2,Bungalow premium,"4 beds (2 twins, 1 full, 1 sofa bed)",205.038,Malgrat de Mar,Spain,2,1,0,0,0,0,0,4
+Vacaciones Oromarina Jardines del Mar,"Marina d’Or Holiday Resort Area, Oropesa del Mar",7.3,0.0,Standard Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",213.044,Oropesa del Mar,Spain,2,1,0,0,0,0,0,4
+Apartamentos Juan Benítez,El Cotillo,8.6,8.8,Apartment with Sea View 2,1 queen bed,192.433,El Cotillo,Spain,0,0,1,0,0,0,0,1
+Montemar,Llanes,9.2,9.5,Gastronomic Offer,2 twin beds,401.136,Llanes,Spain,2,0,0,0,0,0,0,2
+Occidental Jandía Playa,Morro del Jable,7.9,8.3,Family Room with Sea View,2 king beds,389.53700000000003,Morro del Jable,Spain,0,0,0,2,0,0,0,2
+Mercure Benidorm,Benidorm,9.0,9.4,Deluxe Double Room with Terrace,1 queen bed,417.838,Benidorm,Spain,0,0,1,0,0,0,0,1
+Hotel Mediterráneo,Guardamar del Segura,8.2,8.4,Double or Twin Room,   1 double or 2 twins,187.284,Guardamar del Segura,Spain,2,0,0,0,0,0,0,2
+Apartaments Palamós - Pal Beach,Palamós,8.1,0.0,One-Bedroom Apartment with Balcony or Terrace (2 - 4 Adults),"3 beds (2 twins, 1 sofa bed)",163.264,Palamós,Spain,2,0,0,0,0,0,0,3
+Agroturismo Son Vives Menorca - Adults Only,Ferreries,9.7,9.8,Superior Double Room,1 queen bed,650.62,Ferreries,Spain,0,0,1,0,0,0,0,1
+Apartamentos Centremar,L'Estartit,8.7,9.0,Two-Bedroom Apartment Street View (3 Adults + 2 Children),"5 beds (4 twins, 1 sofa bed)",180.322,L'Estartit,Spain,4,0,0,0,0,0,0,5
+"Hotel Marqués, Blue Hoteles","Gijon City Centre, Gijón",8.3,8.6,Special Offer - Double Room,   1 double or 2 twins,173.882,Gijón,Spain,2,0,0,0,0,0,0,2
+Sol Tenerife,Playa de las Americas,7.1,0.0,Twin Room,2 twin beds,296.011,Playa de las Americas,Spain,2,0,0,0,0,0,0,2
+Apartamentos Tapahuga,Playa de Santiago,8.7,9.0,Apartment with Mountain View,"3 beds (2 twins, 1 sofa bed)",231.494,Santiago,Spain,2,0,0,0,0,0,0,3
+Oassium Hotel - Adults Only,La Pineda,8.9,9.1,Twin Room,2 twin beds,196.523,La Pineda,Spain,2,0,0,0,0,0,0,2
+Balneario de Mondariz,Mondariz-Balneario,7.8,8.1,Double Room with Spa Access,   1 double or 2 twins,292.414,Mondariz-Balneario,Spain,2,0,0,0,0,0,0,2
+Barceló Sevilla Renacimiento,"Triana, Seville",8.7,9.1,Habitación Deluxe Premium,"2 beds (1 king, 1 sofa bed)",544.818,Seville,Spain,0,0,0,1,0,0,0,2
+Hotel Blancafort Spa Termal,La Garriga,7.5,0.0,Double or Twin Room with Spa Access,   1 double or 2 twins,373.524,La Garriga,Spain,2,0,0,0,0,0,0,2
+NH Campo Cartagena,Cartagena,8.1,8.7,Standard Double or Twin Room,   1 double or 2 twins,161.297,Cartagena,Spain,2,0,0,0,0,0,0,2
+Valentina Beach,Las Palmas de Gran Canaria,8.5,8.6,Senior Suite with Terrace and Jacuzzi,"3 beds (1 full, 1 sofa bed, 1 queen)",383.354,Las Palmas,Spain,0,1,1,0,0,0,0,3
+Crisol Monasterio de San Miguel,El Puerto de Santa María,8.1,8.4,Double or Twin Room,   1 double or 2 twins,277.097,El Puerto de Santa María,Spain,2,0,0,0,0,0,0,2
+Hotel K10,Urnieta,8.5,8.9,Superior Double Room,1 king bed,310.283,Urnieta,Spain,0,0,0,1,0,0,0,1
+Círculo Artístico 1911 Hotel Boutique,Caravaca de la Cruz,9.0,9.3,Deluxe Queen Room,1 queen bed,429.541,Caravaca de la Cruz,Spain,0,0,1,0,0,0,0,1
+La Siesta,Villamarciel,8.1,8.5,Standard Double Room,1 queen bed,137.504,Villamarciel,Spain,0,0,1,0,0,0,0,1
+Hotel Palacio Marqués de Arizón,Sanlúcar de Barrameda,8.5,9.0,Double or Twin Room,   1 double or 2 twins,268.974,Sanlúcar de Barrameda,Spain,2,0,0,0,0,0,0,2
+Residencial Marina de Port,L'Ametlla de Mar,8.8,8.5,Three-Bedroom Duplex Apartment with Sea View,"6 beds (4 twins, 1 bunk bed, 1 sofa bed)",355.892,L'Ametlla de Mar,Spain,4,0,0,0,0,0,0,6
+Senator Gandia Spa Hotel,Gandía,8.7,9.1,Double Room,1 queen bed,207.785,Gandía,Spain,0,0,1,0,0,0,0,1
+Hotel Capri,Port de Pollensa,8.5,8.7,Economy Double Room,2 twin beds,285.799,Port de Pollensa,Spain,2,0,0,0,0,0,0,2
+TRH Mijas,Mijas,8.1,8.2,Twin Room,2 twin beds,307.104,Mijas,Spain,2,0,0,0,0,0,0,2
+Vendaval,Santa Marta de Ortigueira,8.8,9.1,Standard Double,1 full bed,187.632,Santa Marta de Ortigueira,Spain,0,1,0,0,0,0,0,1
+Daniya Denia Spa & Business 4*,Denia,7.7,8.1,Junior Suite with Garden View,2 twin beds,415.349,Denia,Spain,2,0,0,0,0,0,0,2
+Pierre & Vacances Almeria Roquetas de Mar,Roquetas de Mar,8.0,8.1,One-Bedroom Apartment (2 - 4 Adults),"2 beds (1 full, 1 sofa bed)",134.568,Roquetas de Mar,Spain,0,1,0,0,0,0,0,2
+Apartments at Cala Blanca,Taurito,8.4,8.9,One-Bedroom Apartment (3 Adults),"3 beds (2 twins, 1 sofa bed)",173.684,Taurito,Spain,2,0,0,0,0,0,0,3
+Golf Lovers Home,San Miguel de Salinas,0.0,0.0,One-Bedroom Apartment,1 full bed,170.57500000000002,San Miguel de Salinas,Spain,0,1,0,0,0,0,0,1
+Hotel Alda Castillo de Olite,Olite,8.5,8.8,Standard Double Room,   1 double or 2 twins,190.156,Olite,Spain,2,0,0,0,0,0,0,2
+Silken Río Santander,"El Sardinero Beach, Santander",8.3,8.7,Standard Double or Twin Room,   1 double or 2 twins,275.82,Santander,Spain,2,0,0,0,0,0,0,2
+NH Castellón Mindoro,Castellón de la Plana,8.1,8.5,Standard Double or Twin Room,   1 double or 2 twins,219.427,Castellón de la Plana,Spain,2,0,0,0,0,0,0,2
+"Holiday Inn Express Campo de Gibraltar-Barrios, an IHG Hotel",Los Barrios,8.2,8.6,Double or Twin Room,   1 double or 2 twins,145.249,Los Barrios,Spain,2,0,0,0,0,0,0,2
+SPA Sercotel Odeón,Ferrol,8.2,8.8,Double or Twin Room,2 twin beds,150.268,Ferrol,Spain,2,0,0,0,0,0,0,2
+Hotel Auditorio Santiago & Spa,Lugo,8.2,8.6,Double or Twin Room with Spa Access,2 twin beds,161.872,Lugo,Spain,2,0,0,0,0,0,0,2
+Grupotel Playa de Palma Suites & Spa,Playa de Palma,8.9,9.3,Junior Suite with Pool View (2 Adults),2 twin beds,474.197,Palma de Mallorca,Spain,2,0,0,0,0,0,0,2
+Hotel Gardu,Montealegre del Castillo,7.2,0.0,Studio with Spa Bath,1 king bed,243.678,Montealegre del Castillo,Spain,0,0,0,1,0,0,0,1
+Hotel Spa Galatea,Burela de Cabo,8.0,8.4,Superior Double Room,1 full bed,162.046,Burela de Cabo,Spain,0,1,0,0,0,0,0,1
+La Marine,Melenara,8.8,9.2,Apartment with Beach View (No Jacuzzi),"2 beds (1 sofa bed, 1 queen)",255.862,Melenara,Spain,0,0,1,0,0,0,0,2
+AC Hotel Palencia by Marriott,Palencia,8.3,8.9,"Standard Plus Twin Room, Guest room, 2 Twin/Single Bed(s)",2 twin beds,242.39000000000001,Palencia,Spain,2,0,0,0,0,0,0,2
+Hotel Los Kekes,Coria,8.1,8.4,Standard Triple Room,3 twin beds,109.655,Coria,Spain,3,0,0,0,0,0,0,3
+"Hotel Las Águilas Tenerife, Affiliated by Meliá",Puerto de la Cruz,8.2,8.5,Suite - Ground Floor,2 twin beds,219.91400000000002,Puerto de la Cruz,Spain,2,0,0,0,0,0,0,2
+NH Ciudad de Cuenca,Cuenca,8.6,9.0,Standard Double or Twin Room,   1 double or 2 twins,217.397,Cuenca,Spain,2,0,0,0,0,0,0,2
+Aparthotel Pierre & Vacances Mallorca Cecilia,Portocolom,8.2,8.4,Studio (2 Adults),1 sofa bed,144.078,Portocolom,Spain,0,0,0,0,0,0,0,0
+Casa Flor,Raso,8.5,8.9,Four-Bedroom House,"5 beds (2 twins, 2 fulls, 1 sofa bed)",171.135,Raso,Spain,2,2,0,0,0,0,0,5
+Hotel Jonquera,La Jonquera,7.3,0.0,Double Room with Extra Bed,3 twin beds,190.118,La Jonquera,Spain,3,0,0,0,0,0,0,3
+Payet Apartments,L'Estartit,8.0,0.0,One-Bedroom Apartment (2 - 4 Adults),"3 beds (2 twins, 1 sofa bed)",124.276,L'Estartit,Spain,2,0,0,0,0,0,0,3
+Exe Estepona Thalasso & Spa- Adults Only,Estepona,8.3,8.8,Deluxe Double Room,   1 double or 2 twins,281.692,Estepona,Spain,2,0,0,0,0,0,0,2
+Casas Rurales Noguericas,Archivel,8.4,8.5,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",199.816,Archivel,Spain,2,1,0,0,0,0,0,3
+Hotel Rural El Angel de la Guarda,Güemes,9.2,9.3,Double Room with Terrace and Spa Bath,1 full bed,215.365,Güemes,Spain,0,1,0,0,0,0,0,1
+Complejo Rural El Molinillo,Arenas del Rey,8.8,8.7,Two-Bedroom Villa,"4 beds (2 twins, 1 full, 1 queen)",170.57500000000002,Arenas del Rey,Spain,2,1,1,0,0,0,0,4
+Hotel Las Rampas,"El Castillo Beach, Fuengirola",8.3,8.6,Double or Twin Room,   1 double or 2 twins,177.986,Fuengirola,Spain,2,0,0,0,0,0,0,2
+Ona Club Bena Vista,Estepona,8.6,8.9,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",176.005,Estepona,Spain,2,0,0,0,0,0,0,3
+Hotel Aloe Canteras,Las Palmas de Gran Canaria,8.6,8.9,Deluxe Double Room with Sea View,2 twin beds,288.786,Las Palmas,Spain,2,0,0,0,0,0,0,2
+ALCAMAR Habitaciones en Pisos compartidos cerca al Mar!,Guía de Isora,8.2,8.5,Small Double Room,1 full bed,69.726,Guía de Isora,Spain,0,1,0,0,0,0,0,1
+Apartamentos Vacacionales Broncemar,San Bartolomé,7.1,0.0,One-Bedroom Apartment,1 full bed,165.005,San Bartolomé,Spain,0,1,0,0,0,0,0,1
+Aparthotel Ona Aucanada,Port d'Alcudia,8.6,8.8,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",359.919,Port d'Alcudia,Spain,2,1,0,0,0,0,0,4
+Hotel Sa Barrera - Adults Only,Cala en Porter,8.5,8.9,Deluxe Double or Twin Room,   1 double or 2 twins,567.517,Cala en Porter,Spain,2,0,0,0,0,0,0,2
+Hotel Benalma Costa del Sol,Benalmádena,8.5,8.9,Gran Sea View Double Room (2 Adults +2 Children),"3 beds (2 twins, 1 sofa bed)",619.141,Benalmádena,Spain,2,0,0,0,0,0,0,3
+Hotel Ribera,Santiago de la Ribera,8.2,8.1,Twin Room,2 twin beds,136.808,Santiago de la Ribera,Spain,2,0,0,0,0,0,0,2
+Corralejo Surfing Colors Hotel&Apartments,Corralejo,8.5,8.7,Surfing Dreams Room,2 twin beds,190.556,Corralejo,Spain,2,0,0,0,0,0,0,2
+Hotel Cerro La Nina,Beceña,8.7,9.0,Double or Twin Room,   1 double or 2 twins,176.957,Beceña,Spain,2,0,0,0,0,0,0,2
+Casa Rural La Plazuela,Cazalla de la Sierra,8.0,8.2,Twin Room,2 twin beds,146.207,Cazalla de la Sierra,Spain,2,0,0,0,0,0,0,2
+Ramada by Wyndham Valencia Almussafes,Almussafes,8.1,8.7,Twin Room - Non-Smoking,2 twin beds,301.494,Almussafes,Spain,2,0,0,0,0,0,0,2
+Lopesan Costa Meloneras Resort & Spa,Meloneras,8.8,9.4,Deluxe Vista (2+0),1 king bed,778.89,Meloneras,Spain,0,0,0,1,0,0,0,1
+Axel Hotel San Sebastián - Adults Only,"San Sebastian City Center, San Sebastián",8.4,9.1,Standard Double Room,   1 double or 2 twins,374.812,San Sebastián,Spain,2,0,0,0,0,0,0,2
+El Mesón de Despeñaperros,Santa Elena,8.4,8.5,Quadruple,4 twin beds,219.31,Santa Elena,Spain,4,0,0,0,0,0,0,4
+Hotel Rural El Navío - Adults Only,Alcalá,8.9,8.8,Budget Twin Room,2 twin beds,243.678,Alcalá,Spain,2,0,0,0,0,0,0,2
+"Casa Uwe, El Remo",Puerto Naos,9.0,8.3,Apartment with Sea View,"4 beds (2 twins, 1 full, 1 sofa bed)",197.031,Puerto Naos,Spain,2,1,0,0,0,0,0,4
+Hotel Patricia,Torreguadiaro,7.5,0.0,Double Room,1 full bed,208.588,Torreguadiaro,Spain,0,1,0,0,0,0,0,1
+Teide Rooms,Puerto de Sagunto,8.0,8.6,Double Room with Balcony,1 full bed,175.814,Puerto de Sagunto,Spain,0,1,0,0,0,0,0,1
+Eurostars Arenas de Pinto,Pinto,8.6,8.9,Double or Twin Room,Multiple bed types,258.183,Pinto,Spain,0,0,0,0,0,0,0,0
+Apartamentos la Higar,La Pesa,8.3,8.3,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",266.88,La Pesa,Spain,2,0,0,0,0,0,0,3
+Sancho Ramirez,Pamplona,8.1,8.5,Double Room,   1 double or 2 twins,261.084,Pamplona,Spain,2,0,0,0,0,0,0,2
+Hotel Olympus Palace,"Salou City Centre, Salou",8.7,9.1,Double Room with Pool View,   1 double or 2 twins,268.394,Salou,Spain,2,0,0,0,0,0,0,2
+Senator Marbella Spa Hotel,Marbella,7.8,8.3,Superior Double or Twin Room with Sea View,   1 double or 2 twins,376.606,Marbella,Spain,2,0,0,0,0,0,0,2
+Alua Leo,Can Pastilla,8.1,8.4,Double Room with Pool View,"3 beds (2 twins, 1 sofa bed)",319.914,Can Pastilla,Spain,2,0,0,0,0,0,0,3
+Balneario Cervantes,Santa Cruz de Mudela,7.3,0.0,Standard Double Room,2 twin beds,144.93,Santa Cruz de Mudela,Spain,2,0,0,0,0,0,0,2
+Hotel Mirador Jorquera,Jorquera,8.6,9.0,Standard Double Room,   1 double or 2 twins,252.52,Jorquera,Spain,2,0,0,0,0,0,0,2
+Hotel Florida Magaluf - Adults Only,Magaluf,8.8,9.0,Double or Twin Room with Sea View,2 twin beds,399.154,Magaluf,Spain,2,0,0,0,0,0,0,2
+"7Pines Resort Ibiza, part of Destination by Hyatt",San Jose,8.6,9.1,Resort Suite,1 king bed,1062.436,San Jose,Spain,0,0,0,1,0,0,0,1
+Hotel BCL Levante Club & Spa - Only Adults Recomended,"Rincon de Loix, Benidorm",8.5,9.0,Double or Twin Room with Balcony (2 Adults),   1 double or 2 twins,317.517,Benidorm,Spain,2,0,0,0,0,0,0,2
+Hotel-Restaurante La Sima,Castillo de Garcimuñoz,7.9,8.2,Twin Room with Bathroom,2 twin beds,121.839,Castillo de Garcimuñoz,Spain,2,0,0,0,0,0,0,2
+casa Decklid golf apartment,Alhama de Murcia,8.9,8.9,Three-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",184.934,Alhama de Murcia,Spain,2,1,0,0,0,0,0,4
+Hotel Naranjo De Bulnes,Arenas de Cabrales,8.3,8.4,Triple Room,Multiple bed types,222.815,Arenas de Cabrales,Spain,0,0,0,0,0,0,0,0
+Dreams Jardin Tropical Resort & Spa,Adeje,8.6,8.9,Double Room,"3 beds (2 twins, 1 sofa bed)",459.507,Adeje,Spain,2,0,0,0,0,0,0,3
+Hotel Hacienda de Abajo-Adults Only,Tazacorte,9.2,9.3,Premium Double Room,   1 double or 2 twins,669.961,Tazacorte,Spain,2,0,0,0,0,0,0,2
+Hotel Abeiras,O Grove,8.7,9.0,Twin Room,2 twin beds,148.214,O Grove,Spain,2,0,0,0,0,0,0,2
+Apartamentos Rurales Campillo,Arroyo Frio,8.6,8.2,3-Bedroom Apartment,"5 beds (4 twins, 1 full)",244.084,Arroyo Frio,Spain,4,1,0,0,0,0,0,5
+Hotel & Restaurante Peña,Arrós,8.8,9.2,Economy Double Room,   1 double or 2 twins,197.486,Arrós,Spain,2,0,0,0,0,0,0,2
+Hotel Real,Lleida,8.4,8.8,Twin Room,2 twin beds,203.877,Lleida,Spain,2,0,0,0,0,0,0,2
+Apartamentos Vista Alegre Mallorca,Porto Cristo,8.6,8.6,Apartment with Pool View,"3 beds (2 twins, 1 sofa bed)",238.787,Porto Cristo,Spain,2,0,0,0,0,0,0,3
+Finca Cortesin Hotel Golf & Spa,Casares,9.6,9.9,Executive Suite with Sea View and Spa Access,1 king bed,4149.14,Casares,Spain,0,0,0,1,0,0,0,1
+Casa Rural - Suerte (+Piscina),Illana,8.9,8.6,Three-Bedroom Villa,"6 beds (2 twins, 2 fulls, 1 king, 1 sofa bed)",753.9970000000001,Illana,Spain,2,2,0,1,0,0,0,6
+Hostal Doris,Colònia de Sant Jordi,9.2,9.4,Twin Room,2 twin beds,165.04,Colònia de Sant Jordi,Spain,2,0,0,0,0,0,0,2
+C.T.R. Camino de la Fuentona,Cabrejas del Pinar,9.3,9.1,Double or Twin Room,2 twin beds,141.821,Cabrejas del Pinar,Spain,2,0,0,0,0,0,0,2
+Lopesan Villa del Conde Resort & Thalasso,Meloneras,8.7,9.3,Double or Twin Room (2 Adults),1 king bed,511.966,Meloneras,Spain,0,0,0,1,0,0,0,1
+Vivienda familiar en A Guarda,A Guarda,9.4,9.7,Three-Bedroom Apartment,"4 beds (2 twins, 1 king, 1 queen)",222.443,A Guarda,Spain,2,0,1,1,0,0,0,4
+Hotel El Patio,Garachico,8.7,8.8,Basic Double or Twin Room - 1640 feet from main building,   1 double or 2 twins,295.73900000000003,Garachico,Spain,2,0,0,0,0,0,0,2
+Occidental Fuengirola,"El Castillo Beach, Fuengirola",8.5,9.1,Superior Double or Twin Room with Lateral Sea View,Multiple bed types,515.4,Fuengirola,Spain,0,0,0,0,0,0,0,0
+Hotel Medium Sitges Park,"Sitges Town Center, Sitges",8.2,8.5,Double or Twin Room,   1 double or 2 twins,326.877,Sitges,Spain,2,0,0,0,0,0,0,2
+Hoposa Costa D'or - Adults Only,Deia,8.9,9.2,Double or Twin Room,   1 double or 2 twins,627.54,Deia,Spain,2,0,0,0,0,0,0,2
+Hotel Malcom and Barret,"Quatre Carreres, Valencia",8.7,9.0,Double Room,Multiple bed types,519.266,Valencia,Spain,0,0,0,0,0,0,0,0
+Amanecer Cabrerizos,Cabrerizos,9.8,9.9,Two-Bedroom House,"4 beds (1 twin, 2 fulls, 1 sofa bed)",543.054,Cabrerizos,Spain,1,2,0,0,0,0,0,4
+Porcel Avant,Torrejón de Ardoz,8.3,8.6,Double Room with parking included,   1 double or 2 twins,227.711,Torrejón de Ardoz,Spain,2,0,0,0,0,0,0,2
+Ona Alanda Club Marbella,"Nikki Beach, Marbella",8.7,9.0,Superior Two-Bedroom Apartment (6 Adults),"5 beds (4 twins, 1 sofa bed)",519.668,Marbella,Spain,4,0,0,0,0,0,0,5
+Hotel Spa Norat O Grove 3* Superior,O Grove,8.3,8.6,Double Room,   1 double or 2 twins,140.52100000000002,O Grove,Spain,2,0,0,0,0,0,0,2
+Camping Torre de la Mora,Tamarit,8.5,8.4,Tent (Foret 2 pax),1 full bed,120.795,Tamarit,Spain,0,1,0,0,0,0,0,1
+El Marques Palace by Intercorp Group,Puerto de Santiago,8.5,8.5,Two-Bedroom Apartment with Garden View,"4 beds (2 twins, 1 full, 1 sofa bed)",250.64000000000001,Puerto de Santiago,Spain,2,1,0,0,0,0,0,4
+Macdonald Leila Playa Resort,La Cala de Mijas,9.0,9.1,Classic One-Bedroom Apartment with Terrace,"3 beds (2 twins, 1 sofa bed)",341.641,La Cala de Mijas,Spain,2,0,0,0,0,0,0,3
+Hotel Bahía de Vigo,Vigo,8.2,8.6,Double or Twin Room with Sea View,   1 double or 2 twins,252.729,Vigo,Spain,2,0,0,0,0,0,0,2
+Hotel VIDA Finisterre Centro,Finisterre,8.8,9.2,Double Room - Attic,1 full bed,150.008,Finisterre,Spain,0,1,0,0,0,0,0,1
+Art Suites by Casa de Indias,El Puerto de Santa María,9.3,9.5,Deluxe King Room,1 queen bed,466.409,El Puerto de Santa María,Spain,0,0,1,0,0,0,0,1
+NH Campo de Gibraltar,Los Barrios,8.1,8.5,Standard Double or Twin Room,   1 double or 2 twins,151.279,Los Barrios,Spain,2,0,0,0,0,0,0,2
+Ampuria Inn,Empuriabrava,7.9,8.1,Double or Twin Room,   1 double or 2 twins,185.775,Empuriabrava,Spain,2,0,0,0,0,0,0,2
+Apartamentos Manilva Green,Manilva,8.2,8.1,One-Bedroom Apartment with Sea View,"3 beds (2 twins, 1 sofa bed)",159.459,Manilva,Spain,2,0,0,0,0,0,0,3
+Neptuno Suites - Adults Only,Costa Teguise,7.9,8.2,One-Bedroom Apartment with Sea View (2 Adults),2 twin beds,292.448,Costa Teguise,Spain,2,0,0,0,0,0,0,2
+Casas Rurales Los Cortijos,Alcaraz,8.4,8.2,Studio,1 full bed,206.169,Alcaraz,Spain,0,1,0,0,0,0,0,1
+Hotel plaza,Riaza,7.1,0.0,Double Room,1 full bed,134.023,Riaza,Spain,0,1,0,0,0,0,0,1
+Carema Club Resort,Fornells,7.5,0.0,Promo One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",145.38400000000001,Fornells,Spain,2,0,0,0,0,0,0,3
+Sol Costa Daurada,Salou,7.9,8.0,Double Room,   1 double or 2 twins,141.931,Salou,Spain,2,0,0,0,0,0,0,2
+Chijere San Sebastián,San Sebastián de la Gomera,7.9,8.0,One-Bedroom Apartment Upper Floor,2 twin beds,179.974,San Sebastián de la Gomera,Spain,2,0,0,0,0,0,0,2
+Porcel Alixares,"Granada City Center, Granada",8.3,8.7,Double or Twin Room,2 twin beds,262.225,Granada City Center,Spain,2,0,0,0,0,0,0,2
+Hotel Matilde by Grupo Matilde,Las Palmas de Gran Canaria,8.8,9.1,Superior Family Room,   1 double or 2 twins,208.749,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Mamboo Hotel Cala Ratjada,Cala Ratjada,9.1,9.2,Double or Twin Room with Extra Bed,1 queen bed,221.552,Cala Ratjada,Spain,0,0,1,0,0,0,0,1
+Apartamentos Marina,Naveces,8.1,8.4,Studio (6 Adults),"3 beds (1 full, 2 sofa beds)",244.258,Naveces,Spain,0,1,0,0,2,0,0,3
+Hotel Noia,Noya,8.9,9.2,Standard Double Room,1 full bed,172.431,Noya,Spain,0,1,0,0,0,0,0,1
+Hostal Mourelos,O Grove,7.7,0.0,Double Room,   1 double or 2 twins,92.598,O Grove,Spain,2,0,0,0,0,0,0,2
+Meliá Fuerteventura,Costa Calma,7.9,8.2,Melia Guestroom with Sea View,2 twin beds,328.153,Costa Calma,Spain,2,0,0,0,0,0,0,2
+Zoetry Mallorca Wellness & Spa,Llucmajor,8.8,9.3,Deluxe Room at Ponent Buildings (2 Adults),2 twin beds,829.201,Llucmajor,Spain,2,0,0,0,0,0,0,2
+Calpe Sunsea Village,Calpe,7.3,0.0,Three-Bedroom Apartment,6 twin beds,197.263,Calpe,Spain,6,0,0,0,0,0,0,6
+Bungalows Conca De Ter,Vilallonga de Ter,8.3,8.0,Filandés Bungalow (4 Adults),"2 beds (1 full, 1 bunk bed)",281.274,Vilallonga de Ter,Spain,0,1,0,0,0,0,0,2
+RS Jardin del Pintor VDE 066,Jacarilla,8.4,9.2,Apartment,"3 beds (2 twins, 1 full)",121.839,Jacarilla,Spain,2,1,0,0,0,0,0,3
+Altamira Camping Park,Queveda,7.6,0.0,Two-Bedroom Bungalow (4 Adults),"3 beds (2 twins, 1 full)",221.096,Queveda,Spain,2,1,0,0,0,0,0,3
+Crown Resorts Club Marbella,La Cala de Mijas,8.1,8.2,Apartment (1-2 Adults),1 full bed,267.431,La Cala de Mijas,Spain,0,1,0,0,0,0,0,1
+BALNEARIO DE RETORTILLO,Retortillo,7.5,0.0,Double or Twin Room with Balcony,"3 beds (2 twins, 1 queen)",150.471,Retortillo,Spain,2,0,1,0,0,0,0,3
+Apartamentos Colón Playa,Las Palmas de Gran Canaria,8.1,8.3,Exterior Studio,2 twin beds,184.963,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Apartamentos Igramar MorroJable - Adults Only,Morro del Jable,8.9,9.0,Apartment with Sea View,"3 beds (2 twins, 1 sofa bed)",351.868,Morro del Jable,Spain,2,0,0,0,0,0,0,3
+Apartamentos VIDA Corcubión,Corcubión,8.7,8.8,Two-Bedroom Apartment (5 Adults),"4 beds (2 twins, 1 full, 1 sofa bed)",180.565,Corcubión,Spain,2,1,0,0,0,0,0,4
+A Morada do Cigarron,Verín,8.1,8.2,Double Room,   1 double or 2 twins,95.034,Verín,Spain,2,0,0,0,0,0,0,2
+Hostal Mulhacen,Trevélez,7.7,0.0,Deluxe King Room,1 king bed,150.848,Trevélez,Spain,0,0,0,1,0,0,0,1
+Hotel Cordoba Center,"Vial Norte, Córdoba",8.9,9.2,Twin Room with Extra Bed,3 twin beds,651.142,Córdoba,Spain,3,0,0,0,0,0,0,3
+Portomar Apartments,Portocolom,8.8,9.2,Apartment with lateral Sea View,"2 beds (1 full, 1 sofa bed)",297.973,Portocolom,Spain,0,1,0,0,0,0,0,2
+Hotel Peñiscola Palace,"Norte Beach, Peniscola",8.7,9.1,Twin Room with Sea View,2 twin beds,284.639,Peniscola,Spain,2,0,0,0,0,0,0,2
+Amita Hotel Boutique,Suances,8.5,8.7,Deluxe Double Room with Spa Bath and Balcony with Sea View,1 queen bed,323.628,Suances,Spain,0,0,1,0,0,0,0,1
+Hotel El Guerra,Güéjar-Sierra,8.3,8.4,Double or Twin Room,2 twin beds,172.315,Güéjar-Sierra,Spain,2,0,0,0,0,0,0,2
+Camping Urbion,Abejar,8.2,8.4,Superior Bungalow,"4 beds (2 twins, 2 fulls)",291.253,Abejar,Spain,2,2,0,0,0,0,0,4
+Hotel Aubí,Sant Antoni de Calonge,8.2,8.5,Standard Double Room,1 full bed,152.544,Sant Antoni de Calonge,Spain,0,1,0,0,0,0,0,1
+Be Live Experience Costa Palma,"Cala Major, Palma de Mallorca",7.7,8.0,Deluxe Twin Room with Sea View - VIP,2 twin beds,534.815,Palma de Mallorca,Spain,2,0,0,0,0,0,0,2
+Sol Port Cambrils Hotel,Cambrils,8.5,9.0,Standard Room,   1 double or 2 twins,200.02100000000002,Cambrils,Spain,2,0,0,0,0,0,0,2
+Hotel y Apartamentos SNÖ Isaba,Isaba,7.7,0.0,Apartment (4 Adults),"3 beds (2 twins, 1 sofa bed)",221.167,Isaba,Spain,2,0,0,0,0,0,0,3
+Aparthotel Novo Resort,Novo Sancti Petri,9.1,9.3,Luxury Two-Bedroom Apartment,"3 beds (2 twins, 1 queen)",628.341,Novo Sancti Petri,Spain,2,0,1,0,0,0,0,3
+Apartamentos Buga,Posada,8.7,8.6,Two-Bedroom Duplex Apartment,"3 beds (2 twins, 1 full)",284.059,Posada,Spain,2,1,0,0,0,0,0,3
+Hotel Playa San Cristóbal,Almuñécar,7.8,8.1,Standard Double Room,2 twin beds,159.55100000000002,Almuñécar,Spain,2,0,0,0,0,0,0,2
+Finca Floretta,Canillas de Albaida,9.4,10.0,Villa,"11 beds (9 twins, 2 bunk beds)",614.069,Canillas de Albaida,Spain,9,0,0,0,0,2,0,11
+Hotel Bell Repos,Platja d'Aro,8.1,8.3,Double or Twin Room with Garden View,   1 double or 2 twins,226.156,Platja d'Aro,Spain,2,0,0,0,0,0,0,2
+Hotel Mesón Kubano B&B,Sant Mateu,8.1,8.1,Basic Triple Room,"2 beds (1 twin, 1 full)",187.632,Sant Mateu,Spain,1,1,0,0,0,0,0,2
+Hotel La Caminera Club de Campo,Torrenueva,9.2,9.8,Classic Double Room with Spa Access,1 king bed,476.332,Torrenueva,Spain,0,0,0,1,0,0,0,1
+Hotel Mediterraneo,"Rincon de Loix, Benidorm",8.5,8.8,Double or Twin Room,2 twin beds,264.377,Benidorm,Spain,2,0,0,0,0,0,0,2
+Conjunto Hotelero La Pasera,Soto de Cangas,7.8,0.0,Double or Twin Room,   1 double or 2 twins,143.306,Soto de Cangas,Spain,2,0,0,0,0,0,0,2
+Catalonia Atenas,"Sant Martí, Barcelona",8.1,8.5,Double or Twin Room,   1 double or 2 twins,590.629,Barcelona,Spain,2,0,0,0,0,0,0,2
+Blau Colònia Sant Jordi,Colònia de Sant Jordi,8.2,8.7,Superior Double or Twin Room,2 twin beds,399.534,Colònia de Sant Jordi,Spain,2,0,0,0,0,0,0,2
+Cortijo Sabila,Villanueva del Rosario,8.8,9.1,Superior Suite with Mountain View,1 queen bed,365.517,Villanueva del Rosario,Spain,0,0,1,0,0,0,0,1
+Hotel Alfar,Isla,7.7,0.0,Two-Bedroom Apartment (4 Adults + 2 Children),"4 beds (2 twins, 1 full, 1 sofa bed)",250.988,Isla,Spain,2,1,0,0,0,0,0,4
+Hotel Helios Lloret,"Lloret Town Center, Lloret de Mar",8.7,9.2,Double or Twin Room,2 twin beds,130.078,Lloret de Mar,Spain,2,0,0,0,0,0,0,2
+Don Pedro,Avilés,8.6,8.9,Double Room,"3 beds (2 twins, 1 full)",178.697,Avilés,Spain,2,1,0,0,0,0,0,3
+Apartamentos El Prado,Valbona,9.1,9.1,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",264.565,Valbona,Spain,2,1,0,0,0,0,0,3
+LACASA Apartments Cotillo,El Cotillo,9.2,9.4,One Bedroom Superior Apartment,"3 beds (2 twins, 1 sofa bed)",213.358,El Cotillo,Spain,2,0,0,0,0,0,0,3
+Veramar,Fuengirola,8.2,8.3,Studio (2 Adults),2 twin beds,221.347,Fuengirola,Spain,2,0,0,0,0,0,0,2
+BlueBay Banús,"Puerto Banus, Marbella",7.3,0.0,Family Room,"3 beds (2 twins, 1 full)",583.297,Marbella,Spain,2,1,0,0,0,0,0,3
+Apartment Escor,Segur de Calafell,7.1,0.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",147.25300000000001,Segur de Calafell,Spain,2,1,0,0,0,0,0,4
+Apartamentos Andrea,Calera,8.2,8.1,Studio Apartment with Sea View,2 twin beds,153.517,Calera,Spain,2,0,0,0,0,0,0,2
+Hostal HPC Porto Colom,Portocolom,7.6,8.0,Twin Room with Balcony,2 twin beds,206.256,Portocolom,Spain,2,0,0,0,0,0,0,2
+Carema Beach Menorca,Cala en Bosc,8.2,8.5,Select Apartment,"3 beds (2 twins, 1 sofa bed)",240.09900000000002,Cala en Bosc,Spain,2,0,0,0,0,0,0,3
+Hotel Abades Benacazon,Benacazón,7.7,0.0,Twin Room with Terrace,2 twin beds,231.183,Benacazón,Spain,2,0,0,0,0,0,0,2
+Apartamentos Rurales Azabal,Azabal,8.0,8.1,One-Bedroom Apartment,1 full bed,161.292,Azabal,Spain,0,1,0,0,0,0,0,1
+Sumus Hotel Monteplaya & SPA 4Sup - Adults Only,"Malgrat de Mar Beach, Malgrat de Mar",8.5,9.0,Standard Double Room,   1 double or 2 twins,180.851,Malgrat de Mar,Spain,2,0,0,0,0,0,0,2
+Hostal Sorbas,Sorbas,7.3,0.0,Standard Double or Twin Room,   1 double or 2 twins,138.89600000000002,Sorbas,Spain,2,0,0,0,0,0,0,2
+Càmping Bellsol,"Pineda de Mar Beach, Pineda de Mar",8.5,8.6,Bungalow,1 full bed,134.893,Pineda de Mar,Spain,0,1,0,0,0,0,0,1
+Barceló Marbella,"San Pedro de Alcantara, Marbella",8.1,8.7,Superior Double or Twin Room with Golf View,Multiple bed types,447.259,Marbella,Spain,0,0,0,0,0,0,0,0
+Hostal La Fosca,Palamós,7.4,0.0,Double Room,2 twin beds,200.16400000000002,Palamós,Spain,2,0,0,0,0,0,0,2
+AxelBeach Ibiza Suites Apartments Spa and Beach Club - Adults Only,San Antonio Bay,8.5,8.7,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",341.39300000000003,San Antonio Bay,Spain,2,0,0,0,0,0,0,3
+Apartamentos Varadero On the beach,Cala del Moral,8.3,8.6,Duplex Apartment with Patio,1 queen bed,222.513,Cala del Moral,Spain,0,0,1,0,0,0,0,1
+Hotel y Apartamentos Penarronda Playa,Castropol,9.1,9.1,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",235.555,Castropol,Spain,0,1,0,0,0,0,0,2
+Fuente del Lobo Glamping & Bungalows - Adults Only,Pinos Genil,7.8,8.1,One-Bedroom Bungalow,1 queen bed,282.765,Pinos Genil,Spain,0,0,1,0,0,0,0,1
+Hotel RH Portocristo & Wellness,"Norte Beach, Peniscola",8.5,8.7,Double Room with Side Sea View and Terrace,   1 double or 2 twins,270.559,Peniscola,Spain,2,0,0,0,0,0,0,2
+Hotel Termes Montbrió,Montbrió del Camp,7.8,8.2,Double or Twin Room with Balcony,   1 double or 2 twins,259.697,Montbrió del Camp,Spain,2,0,0,0,0,0,0,2
+Hotel Restaurante La Cañada,Fuencaliente de Lucio,8.1,8.4,Double or Twin Room,1 full bed,136.46,Fuencaliente de Lucio,Spain,0,1,0,0,0,0,0,1
+Hotel Mercado by gaiarooms,"Salamanca City Centre, Salamanca",7.6,0.0,Double or Twin Room,   1 double or 2 twins,163.25300000000001,Salamanca,Spain,2,0,0,0,0,0,0,2
+Rosamar & Spa 4*s,Lloret de Mar,8.0,8.2,Double or Twin Room with Extra Bed (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",208.055,Lloret de Mar,Spain,2,0,0,0,0,0,0,3
+Apartamentos Ubaga,Ezcaray,7.9,8.0,Penthouse Apartment,"2 beds (1 full, 1 sofa bed)",207.126,Ezcaray,Spain,0,1,0,0,0,0,0,2
+Alua Tenerife,Puerto de la Cruz,8.2,8.9,Double Room with Pool View ( 2 adults ),2 twin beds,218.266,Puerto de la Cruz,Spain,2,0,0,0,0,0,0,2
+Iberik Rocallaura Balneari,Vallbona de les Monges,7.9,8.6,Triple Room (2 Adults + 1 Child),3 twin beds,236.542,Vallbona de les Monges,Spain,3,0,0,0,0,0,0,3
+Plaza Alaquas,Alaquas,8.0,8.3,Standard Double or Twin Room,   1 double or 2 twins,261.084,Alaquas,Spain,2,0,0,0,0,0,0,2
+La Zenia,"La Zenia, Playas de Orihuela",8.4,8.2,Apartment with Garden View,"4 beds (3 twins, 1 queen)",273.267,Playas de Orihuela,Spain,3,0,1,0,0,0,0,4
+Apartamentos Fayna,Playa del Ingles,7.5,0.0,Apartment with Balcony,"3 beds (2 twins, 1 sofa bed)",264.512,Playa del Ingles,Spain,2,0,0,0,0,0,0,3
+Sos Ferres D'en Morey,Manacor,9.4,9.4,King Suite with Spa Bath,"4 beds (2 twins, 1 king, 1 sofa bed)",621.379,Manacor,Spain,2,0,0,1,0,0,0,4
+Prestige Sant Marc,"Santa Margarida, Roses",8.0,8.4,Double or Twin Room with View,   1 double or 2 twins,170.725,Roses,Spain,2,0,0,0,0,0,0,2
+Atzavara Hotel & Spa,"Santa Susanna Beach, Santa Susanna",8.8,9.4,Design Doble,   1 double or 2 twins,286.022,Santa Susanna Beach,Spain,2,0,0,0,0,0,0,2
+Hotel Oasis,"City Center, Conil de la Frontera",7.9,8.1,Double or Twin Room,   1 double or 2 twins,133.559,Conil de la Frontera,Spain,2,0,0,0,0,0,0,2
+Silken Coliseum,"Santander City Centre, Santander",8.4,8.8,Standard Double or Twin Room,   1 double or 2 twins,273.848,Santander,Spain,2,0,0,0,0,0,0,2
+eó Corona Cedral,Puerto Rico de Gran Canaria,8.2,8.5,Studio (2 Adults),"3 beds (2 twins, 1 sofa bed)",161.022,Puerto Rico de Gran Canaria,Spain,2,0,0,0,0,0,0,3
+"Hotel Faro, a Lopesan Collection Hotel - Adults Only",Maspalomas,9.1,9.6,Deluxe Double Room with Partial Sea View,1 king bed,941.224,Maspalomas,Spain,0,0,0,1,0,0,0,1
+Valentin Park Club,Paguera,8.6,8.7,Double or Twin Room with Pool View (2 Adults),2 twin beds,341.81100000000004,Paguera,Spain,2,0,0,0,0,0,0,2
+Barceló Isla Canela,Isla Canela,8.4,8.7,Double Room with Terrace and Pool View,2 twin beds,285.572,Isla Canela,Spain,2,0,0,0,0,0,0,2
+Hotel Ayamonte Center,Ayamonte,7.8,8.0,Twin Room,2 twin beds,160.096,Ayamonte,Spain,2,0,0,0,0,0,0,2
+Hotel Spa Villalba,Vilaflor,8.7,9.1,Suite with Spa Bath,"2 beds (1 king, 1 sofa bed)",616.505,Vilaflor,Spain,0,0,0,1,0,0,0,2
+Complejo Turístico Marina Rey,Vera,8.0,0.0,Two-Bedroom Apartment with Terrace,"4 beds (2 twins, 1 sofa bed, 1 queen)",182.236,Vera,Spain,2,0,1,0,0,0,0,4
+Apartamentos Rurales La Vera,Jarandilla de la Vera,8.9,8.9,One-Bedroom Apartment,1 full bed,176.957,Jarandilla de la Vera,Spain,0,1,0,0,0,0,0,1
+Hospes Palacio de Arenales & Spa,Cáceres,9.0,9.4,Deluxe Double Room with Spa Access,   1 double or 2 twins,513.812,Cáceres,Spain,2,0,0,0,0,0,0,2
+Hotel El Águila,Utebo,7.7,8.2,Twin Room,2 twin beds,167.093,Utebo,Spain,2,0,0,0,0,0,0,2
+Hotel ROC Illetas & SPA,Illetas,8.3,8.5,Double Room with Frontal Sea View,2 twin beds,1016.314,Illetas,Spain,2,0,0,0,0,0,0,2
+Globales Verdemar,Santa Ponsa,7.9,8.2,One-Bedroom Apartment with Sea View (4 Adults),"4 beds (2 twins, 2 sofa beds)",314.258,Santa Ponsa,Spain,2,0,0,0,2,0,0,4
+ALEGRIA Maripins,"Malgrat de Mar Beach, Malgrat de Mar",7.8,8.3,Double or Twin Room,   1 double or 2 twins,143.654,Malgrat de Mar,Spain,2,0,0,0,0,0,0,2
+Hotel Ilunion Bilbao,"Ensanche, Bilbao",8.3,8.7,Double Room,2 twin beds,275.202,Bilbao,Spain,2,0,0,0,0,0,0,2
+Sol Pelicanos Ocas,Benidorm,7.5,8.0,Family Room,2 twin beds,317.826,Benidorm,Spain,2,0,0,0,0,0,0,2
+Hotel Monterrey Roses by Pierre & Vacances,"Santa Margarida, Roses",7.7,8.3,Twin Room,2 twin beds,144.18800000000002,Roses,Spain,2,0,0,0,0,0,0,2
+Pierre & Vacances Villa Romana,Tossa de Mar,7.5,0.0,One Bedroom Apartment Ocean View,"3 beds (2 twins, 1 sofa bed)",200.077,Tossa de Mar,Spain,2,0,0,0,0,0,0,3
+Apartamentos Florazar II Ajhory,Cullera,7.5,0.0,Two-Bedroom Apartment with Sea View (4-6 Adults),"6 beds (4 twins, 2 sofa beds)",213.88,Cullera,Spain,4,0,0,0,2,0,0,6
+Hotel Gastronómico Boa Vista,Viveiro,8.3,8.7,Double or Twin Room,   1 double or 2 twins,183.339,Viveiro,Spain,2,0,0,0,0,0,0,2
+"Asia Gardens Hotel & Thai Spa, a Royal Hideaway Hotel",Finestrat,9.0,9.4,Superior Deluxe Double or Twin Room (2 Adults),Multiple bed types,1116.277,Finestrat,Spain,0,0,0,0,0,0,0,0
+Ave del Mar,Camariñas,8.0,8.4,Twin Room,2 twin beds,121.839,Camariñas,Spain,2,0,0,0,0,0,0,2
+Hotel Brismar,Port d'Andratx,7.7,8.1,Double or Twin Room with Balcony,   1 double or 2 twins,340.279,Port d'Andratx,Spain,2,0,0,0,0,0,0,2
+Apartamentos en Leis,Leis,7.9,0.0,Apartment - Split Level,2 full beds,170.57500000000002,Leis,Spain,0,2,0,0,0,0,0,2
+Mesón de Sancho,Tarifa,8.9,9.1,Bungalow (4 Adults),4 twin beds,331.67,Tarifa,Spain,4,0,0,0,0,0,0,4
+Hotel Emblemático San Agustin,Icod de los Vinos,9.5,9.5,Double or Twin Room,2 twin beds,280.23,Icod de los Vinos,Spain,2,0,0,0,0,0,0,2
+Hotel Las Murallas,"Old Town of Avila, Ávila",8.1,8.3,Double or Twin Room,2 twin beds,147.367,Ávila,Spain,2,0,0,0,0,0,0,2
+La Torre del Algas,Nonaspe,9.5,9.7,Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",321.65500000000003,Nonaspe,Spain,2,1,0,0,0,0,0,4
+Hostal HB Torrevieja,Torrevieja,8.0,8.2,Twin Room,2 twin beds,156.07,Torrevieja,Spain,2,0,0,0,0,0,0,2
+Apartaments Sa Torre by SUREDA MAS,Canyamel,9.2,9.2,Two-Bedroom Apartment,"5 beds (4 twins, 1 sofa bed)",266.218,Canyamel,Spain,4,0,0,0,0,0,0,5
+Hotel Condesa,Port d'Alcudia,8.0,8.3,Quadruple Room,   1 double or 2 twins,362.363,Port d'Alcudia,Spain,2,0,0,0,0,0,0,2
+Petit Hotel Forn Nou,Artá,8.7,8.9,Superior Double or Twin Room,   1 double or 2 twins,298.297,Artá,Spain,2,0,0,0,0,0,0,2
+AC Hotel Huelva by Marriott,Huelva,8.3,8.7,Standard Plus Queen Room with City View,1 queen bed,267.337,Huelva,Spain,0,0,1,0,0,0,0,1
+Broncemar Beach Suites,Caleta De Fuste,8.3,8.6,Deluxe Room,"3 beds (2 twins, 1 sofa bed)",218.266,Caleta De Fuste,Spain,2,0,0,0,0,0,0,3
+Camping Ripolles,Ripoll,7.1,0.0,Bungalow (4 Adults),"3 beds (2 twins, 1 full)",321.423,Ripoll,Spain,2,1,0,0,0,0,0,3
+Los Palmitos Peñiscola,Peniscola,7.8,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",128.80100000000002,Peniscola,Spain,0,1,0,0,0,0,0,2
+Hotel Millan,Negreira,6.9,0.0,Twin Room,2 twin beds,185.079,Negreira,Spain,2,0,0,0,0,0,0,2
+L'Azure Hotel 4* Sup,Lloret de Mar,9.0,9.4,Standard Double Room (2 Adults),   1 double or 2 twins,177.18200000000002,Lloret de Mar,Spain,2,0,0,0,0,0,0,2
+El Cau dels Somnis,La Cuevarruz,8.0,8.0,Duplex Apartment,"3 beds (2 twins, 1 queen)",296.845,La Cuevarruz,Spain,2,0,1,0,0,0,0,3
+Can Porretí Agroturisme,Lloret de Vistalegre,9.1,9.4,Deluxe Apartment,"2 beds (1 full, 1 sofa bed)",383.793,Lloret de Vistalegre,Spain,0,1,0,0,0,0,0,2
+Apartamentos Turísticos Eira Do Mar,Aios,8.5,8.5,One-Bedroom Apartment with Sea View,"2 beds (1 full, 1 sofa bed)",187.98,Aios,Spain,0,1,0,0,0,0,0,2
+Apartamentos Ares 3000,Ares,6.8,0.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",129.847,Ares,Spain,2,1,0,0,0,0,0,4
+Complejo turístico Quinta La Espadaña,Bedriñana,8.7,8.6,Apartment with Balcony,"3 beds (2 twins, 1 full)",285.451,Bedriñana,Spain,2,1,0,0,0,0,0,3
+Hotel Exe Reina Isabel,Ávila,8.8,9.1,Double or Twin Room,   1 double or 2 twins,195.174,Ávila,Spain,2,0,0,0,0,0,0,2
+Apartamentos VIDA Finisterre,Finisterre,8.9,9.0,Two-Bedroom Apartment with Balcony,"4 beds (2 twins, 1 full, 1 sofa bed)",180.565,Finisterre,Spain,2,1,0,0,0,0,0,4
+Catalonia Santa Justa,"San Pablo - Santa Justa, Seville",8.7,9.2,Double Room,   1 double or 2 twins,602.987,Seville,Spain,2,0,0,0,0,0,0,2
+Bungalows El Arenal,"Javea Beach, Jávea",8.5,8.5,Studio Apartment,1 full bed,242.518,Jávea,Spain,0,1,0,0,0,0,0,1
+Hotel Palacio Urgoiti,Mungia,8.6,9.1,Superior Double Room with Terrace,1 queen bed,331.51800000000003,Mungia,Spain,0,0,1,0,0,0,0,1
+Parador de Manzanares,Manzanares,8.2,8.7,Double Room,2 twin beds,255.862,Manzanares,Spain,2,0,0,0,0,0,0,2
+Hotel Mainare Playa,"Las Gaviotas Beach, Fuengirola",7.5,8.0,Double or Twin Room with Side Sea View,   1 double or 2 twins,230.798,Fuengirola,Spain,2,0,0,0,0,0,0,2
+Xon's Platja HA,Empuriabrava,6.9,0.0,One-Bedroom Apartment (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",197.954,Empuriabrava,Spain,2,0,0,0,0,0,0,3
+Hotel Hispania,Playa de Palma,8.0,8.1,Double or Twin Room,   1 double or 2 twins,388.173,Palma de Mallorca,Spain,2,0,0,0,0,0,0,2
+Hotel Pamplona Plaza,Pamplona,7.9,8.4,Double or Twin Room,   1 double or 2 twins,286.96,Pamplona,Spain,2,0,0,0,0,0,0,2
+Leonardo Royal Hotel Mallorca,Palmanova,8.1,8.3,   Double Room,   1 double or 2 twins,363.66,Palmanova,Spain,2,0,0,0,0,0,0,2
+Nuria,Tarragona,8.1,8.5,Double or Twin Room with Pool View,   1 double or 2 twins,218.382,Tarragona,Spain,2,0,0,0,0,0,0,2
+piso acogedor con encanto,Navas del Rey,8.5,8.8,Two-Bedroom Apartment,2 full beds,220.35500000000002,Navas del Rey,Spain,0,2,0,0,0,0,0,2
+Hotel Rosamar Garden Resort 4*,Lloret de Mar,7.9,8.0,Double Room with Extra Bed (2 Adults + 1 Child),"2 beds (1 full, 1 sofa bed)",230.334,Lloret de Mar,Spain,0,1,0,0,0,0,0,2
+Hotel Be Live Adults Only Marivent,"Cala Major, Palma de Mallorca",7.4,0.0,Double Room,2 twin beds,448.019,Palma de Mallorca,Spain,2,0,0,0,0,0,0,2
+Holiday Club Vista Amadores,Amadores,8.6,9.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",408.451,Amadores,Spain,2,1,0,0,0,0,0,4
+Hotel Alda Centro Ponferrada,Ponferrada,7.5,0.0,Economy Double or Twin Room,   1 double or 2 twins,128.314,Ponferrada,Spain,2,0,0,0,0,0,0,2
+room007 Select Tetuán,"Old Town, Seville",8.5,8.7,Superior Double Room with Balcony,1 full bed,773.556,Seville,Spain,0,1,0,0,0,0,0,1
+Golden Costa Salou - Adults Only 4* Sup,Salou,9.0,9.4,Double Room with Pool View,1 full bed,238.893,Salou,Spain,0,1,0,0,0,0,0,1
+Royal Sunset Beach Club,Adeje,8.6,9.1,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",597.939,Adeje,Spain,2,1,0,0,0,0,0,4
+La Casa Del Puente,Regules,8.6,9.2,Double Room with Whirlpool,1 full bed,299.26,Regules,Spain,0,1,0,0,0,0,0,1
+Alda Vía de la Plata Rooms,La Bañeza,7.9,0.0,Economy Double or Twin Room,   1 double or 2 twins,103.73100000000001,La Bañeza,Spain,2,0,0,0,0,0,0,2
+Complejo Blue Sea Puerto Resort compuesto por Hotel Canarife y Bonanza Palace,Puerto de la Cruz,7.0,0.0,Double or Twin Room,   1 double or 2 twins,170.018,Puerto de la Cruz,Spain,2,0,0,0,0,0,0,2
+The Agir Springs Hotel by Medplaya,Benidorm,8.5,8.8,Junior Suite,"2 beds (1 full, 1 sofa bed)",379.882,Benidorm,Spain,0,1,0,0,0,0,0,2
+Apartamentos Parque Mar,Cala d´Or,8.6,8.6,Studio (2 Adults),2 twin beds,268.046,Santanyí,Espagne,2,0,0,0,0,0,0,2
+NH Luz Huelva,Huelva,8.2,8.7,Standard Double or Twin Room,1 full bed,219.662,Huelva,Spain,0,1,0,0,0,0,0,1
+The Real Casa Atlantica Morro Jable By PVL,Morro del Jable,7.6,0.0,Studio Apartment with Sea View,"3 beds (2 twins, 1 sofa bed)",135.415,Morro del Jable,Spain,2,0,0,0,0,0,0,3
+Itaca Artemisa by Soho Boutique,"Old Town, Seville",8.0,8.3,Double Room,   1 double or 2 twins,507.19800000000004,Seville,Spain,2,0,0,0,0,0,0,2
+Be Free Carboneras,Carboneras,7.1,0.0,Apartment with Terrace,"2 beds (1 full, 1 sofa bed)",148.254,Carboneras,Spain,0,1,0,0,0,0,0,2
+AR Diamante Beach Spa Hotel,"Fossa-Levante Beach, Calpe",8.4,8.9,Double or Twin Room,Multiple bed types,274.033,Calpe,Spain,0,0,0,0,0,0,0,0
+Apartamentos Sinfony,Canyamel,9.2,9.4,Studio with Balcony (2 Adults),2 twin beds,245.627,Canyamel,Spain,2,0,0,0,0,0,0,2
+Catalonia Reina Victoria,Ronda,9.0,9.3,Double or Twin Room,   1 double or 2 twins,454.866,Ronda,Spain,2,0,0,0,0,0,0,2
+Occidental Torremolinos Playa,"Los Álamos, Torremolinos",7.9,8.4,Family Room,"3 beds (2 twins, 1 sofa bed)",482.482,Torremolinos,Spain,2,0,0,0,0,0,0,3
+Apartments Vista Montaña Roja,Atogo,7.9,8.0,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",114.877,Atogo,Spain,2,0,0,0,0,0,0,3
+NH Collection Palacio de Burgos,Burgos,8.4,9.0,Superior Double or Twin Room with View,   1 double or 2 twins,346.611,Burgos,Spain,2,0,0,0,0,0,0,2
+Eurostars Palacio de Cristal,Oviedo,8.5,8.8,Double Room,Multiple bed types,182.41,Oviedo,Spain,0,0,0,0,0,0,0,0
+Hotel Riu Concordia,Playa de Palma,8.8,9.2,Double Room,   1 double or 2 twins,404.581,Palma de Mallorca,Spain,2,0,0,0,0,0,0,2
+La Mojonera,Isla Plana,8.7,9.0,Standard One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",164.483,Isla Plana,Spain,2,0,0,0,0,0,0,3
+Camelina Suites - Formerly Torrent Bay,San Antonio Bay,8.5,8.7,One-Bedroom Bungalow - Deluxe House,"3 beds (2 twins, 1 sofa bed)",419.474,San Antonio Bay,Spain,2,0,0,0,0,0,0,3
+Nativo Hotel Ibiza,Santa Eularia des Riu,8.8,9.2,Boheme Double Room,"2 beds (1 king, 1 sofa bed)",780.35,Santa Eularia des Riu,Spain,0,0,0,1,0,0,0,2
+Mazarron Country Club Resort,Mazarrón,8.3,8.2,Villa Antojo Deluxe 4 people,4 twin beds,224.184,Mazarrón,Spain,4,0,0,0,0,0,0,4
+Apartamentos A Vivir Sierra Nevada 3000,Sierra Nevada,7.9,8.0,Studio,1 full bed,124.69,Sierra Nevada,Spain,0,1,0,0,0,0,0,1
+Sol Torremolinos - Don Pedro,"Bajondillo, Torremolinos",8.3,8.6,Double or Twin Room with Pool View,Multiple bed types,373.291,Torremolinos,Spain,0,0,0,0,0,0,0,0
+Hostal Residencia Catalina,Palamós,7.9,8.2,Double Room,1 full bed,166.31,Palamós,Spain,0,1,0,0,0,0,0,1
+Hotel Zahara Beach & Spa - Adults Recommended,Zahara de los Atunes,8.6,9.2,Superior Twin Room,   1 double or 2 twins,336.276,Zahara de los Atunes,Spain,2,0,0,0,0,0,0,2
+Finca Marisa,Tinajo,9.2,9.3,Deluxe Suite,1 king bed,528.099,Tinajo,Spain,0,0,0,1,0,0,0,1
+NH Ourense,Ourense,8.5,8.9,Standard Double or Twin Room,   1 double or 2 twins,246.466,Ourense,Spain,2,0,0,0,0,0,0,2
+Hotel Apartamentos Cala Santanyi,Cala Santanyi,8.8,9.0,Superior Apartment,"2 beds (1 sofa bed, 1 queen)",434.761,Cala Santanyi,Spain,0,0,1,0,0,0,0,2
+Canaima - Only Adults,Puerto Rico de Gran Canaria,7.8,0.0,One-Bedroom Apartment ( 3 adults ),"3 beds (2 twins, 1 sofa bed)",163.264,Puerto Rico de Gran Canaria,Spain,2,0,0,0,0,0,0,3
+Hotel Pamplona Villava,Villava,8.4,8.7,Standard Twin Room,2 twin beds,188.167,Villava,Spain,2,0,0,0,0,0,0,2
+Hotel Castelao,Vilagarcia de Arousa,8.0,8.5,Double or Twin Room,   1 double or 2 twins,189.82500000000002,Vilagarcia de Arousa,Spain,2,0,0,0,0,0,0,2
+Eurostars Las Salinas,Caleta De Fuste,8.3,8.8,Junior Suite,2 twin beds,294.734,Caleta De Fuste,Spain,2,0,0,0,0,0,0,2
+Inturotel Cala Azul,Cala d´Or,8.4,8.7,Superior Apartment with Sea View (2 Adults + 2 Children),"4 beds (2 twins, 2 sofa beds)",428.689,Santanyí,Espagne,2,0,0,0,2,0,0,4
+Hotel Na Taconera,Font de Sa Cala,7.8,8.4,Standard Double Room,3 twin beds,244.00900000000001,Font de Sa Cala,Spain,3,0,0,0,0,0,0,3
+Hotel Suites Valles Pasiegos,Saro,8.8,9.3,Suite,1 king bed,252.845,Saro,Spain,0,0,0,1,0,0,0,1
+Holiday Park Santa Ponsa,Santa Ponsa,7.2,0.0,Standard Apartment,"4 beds (2 twins, 2 sofa beds)",170.57500000000002,Santa Ponsa,Spain,2,0,0,0,2,0,0,4
+Hotel Guitart Central Park Aqua Resort,Lloret de Mar,7.2,0.0,Standard Double or Twin Room,2 twin beds,162.336,Lloret de Mar,Spain,2,0,0,0,0,0,0,2
+Eurostars Toledo,Toledo,8.4,8.9,Double or Twin Room,Multiple bed types,273.151,Toledo,Spain,0,0,0,0,0,0,0,0
+Apartamentos BlueBay Beach Club,San Agustin,7.4,0.0,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",188.328,San Agustin,Spain,2,0,0,0,0,0,0,3
+Hotel ECOLOGICO TOTAL,Santa Cruz de Mudela,8.5,8.9,Double or Twin Room,Multiple bed types,192.15800000000002,Santa Cruz de Mudela,Spain,0,0,0,0,0,0,0,0
+La Manga Club Resort - 3 bedroom Duplex - La Colina,Atamaría,0.0,0.0,Three-Bedroom Apartment,"5 beds (4 twins, 1 king)",454.285,Atamaría,Spain,4,0,0,1,0,0,0,5
+Hotel Celta,Vigo,7.1,0.0,Executive Suite,1 full bed,148.644,Vigo,Spain,0,1,0,0,0,0,0,1
+Hotel Honucai,Colònia de Sant Jordi,8.8,9.1,Twin Room,2 twin beds,324.597,Colònia de Sant Jordi,Spain,2,0,0,0,0,0,0,2
+Grupotel Alcudia Suite,Playa de Muro,8.1,8.3,Studio (2 Adults),2 twin beds,208.588,Playa de Muro,Spain,2,0,0,0,0,0,0,2
+Hotel Betriu,Coll de Nargó,8.3,8.6,Double or Twin Room,   1 double or 2 twins,194.942,Coll de Nargó,Spain,2,0,0,0,0,0,0,2
+Pato Amarillo,Punta Umbría,8.2,8.7,Double Room with Sea View,2 twin beds,300.058,Punta Umbría,Spain,2,0,0,0,0,0,0,2
+Exe Agora Cáceres,Cáceres,8.3,8.7,Double Room,   1 double or 2 twins,298.448,Cáceres,Spain,2,0,0,0,0,0,0,2
+Catalonia Giralda,"Old Town, Seville",8.6,9.0,Premium Double or Twin Room,Multiple bed types,617.434,Seville,Spain,0,0,0,0,0,0,0,0
+Exe Auriense,Ourense,7.8,8.3,Double or Twin Room,2 twin beds,177.537,Ourense,Spain,2,0,0,0,0,0,0,2
+FERGUS Style Soller Beach,Port de Soller,8.4,8.8,Standard Double Room (2 Adults),2 twin beds,500.35200000000003,Port de Soller,Spain,2,0,0,0,0,0,0,2
+PATOS BEACH HOUSE,Nigrán,8.2,8.4,Double Room,1 full bed,162.916,Nigrán,Spain,0,1,0,0,0,0,0,1
+Hotel Palacio del Mar,Santander,8.3,8.6,Imperial Suite,1 queen bed,819.4540000000001,Santander,Spain,0,0,1,0,0,0,0,1
+Beach Resort La Margarita,Hospitalet de l'Infant,9.0,8.7,Two-Bedroom House,"3 beds (2 twins, 1 queen)",278.48900000000003,Hospitalet de l'Infant,Spain,2,0,1,0,0,0,0,3
+Hotel Claramar,Platja d'Aro,7.4,0.0,Triple Room with Balcony,Multiple bed types,227.746,Platja d'Aro,Spain,0,0,0,0,0,0,0,0
+Les Suites,Santa María de Palautordera,8.2,8.5,Deluxe King Studio,"2 beds (1 sofa bed, 1 queen)",338.82800000000003,Santa María de Palautordera,Spain,0,0,1,0,0,0,0,2
+Sercotel Puerto de la Luz,Las Palmas de Gran Canaria,8.5,8.8,Classic Room,   1 double or 2 twins,176.291,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Alanda Marbella Hotel,Marbella,8.4,9.0,Premium Deluxe Double Room,   1 double or 2 twins,774.9540000000001,Marbella,Spain,2,0,0,0,0,0,0,2
+Hotel Sierra Luz,Cortegana,8.2,8.5,Villa,8 twin beds,584.827,Cortegana,Spain,8,0,0,0,0,0,0,8
+Mobile Homes by KelAir at Playa Montroig Camping Resort,Tarragona,8.3,8.2,Mobile Home,"4 beds (2 twins, 1 full, 1 bunk bed)",152.29500000000002,Tarragona,Spain,2,1,0,0,0,0,0,4
+Casa Polo,Loiba,8.8,8.1,Three-Bedroom Townhouse (Full Rental),"4 beds (2 twins, 2 fulls)",153.169,Loiba,Spain,2,2,0,0,0,0,0,4
+NUMA HOTEL BOUTIQUE,"Gijon City Centre, Gijón",9.0,9.5,Standard Double or Twin Room,2 twin beds,255.862,Gijón,Spain,2,0,0,0,0,0,0,2
+Apartamentos Chiclana Coto de Sancti Petri,Chiclana de la Frontera,8.2,8.3,Two-Bedroom Apartment,"3 beds (2 twins, 1 full)",287.494,Chiclana de la Frontera,Spain,2,1,0,0,0,0,0,3
+Radisson Blu Resort Gran Canaria,La Playa de Arguineguín,9.0,9.1,Suite- 1 Bedroom & Sea View,"2 beds (1 king, 1 sofa bed)",818.758,Arguineguín,Spain,0,0,0,1,0,0,0,2
+Soho Boutique Cádiz,"Old Town, Cádiz",8.9,9.3,Economy Double Room,Multiple bed types,307.731,Cádiz,Spain,0,0,0,0,0,0,0,0
+Duran Hotel & Restaurant,Figueres,8.5,9.0,Double Room,   1 double or 2 twins,207.358,Figueres,Spain,2,0,0,0,0,0,0,2
+Hotel Gran Canaria Princess - Adults Only,Playa del Ingles,8.2,8.7,Standard Double Room,1 full bed,356.675,Playa del Ingles,Spain,0,1,0,0,0,0,0,1
+H10 Rubicón Palace,Playa Blanca,7.8,8.2,Double or Twin Room (2 Adults),Multiple bed types,528.433,Playa Blanca,Spain,0,0,0,0,0,0,0,0
+Las Villas de Amadores,Puerto Rico de Gran Canaria,8.3,8.6,One-Bedroom Apartment with Balcony,"2 beds (1 full, 1 sofa bed)",264.565,Puerto Rico de Gran Canaria,Spain,0,1,0,0,0,0,0,2
+AC Hotel Gran Canaria by Marriott,Las Palmas de Gran Canaria,8.0,8.4,"Standard Room King, Guest room, 1 King",1 king bed,222.154,Las Palmas,Spain,0,0,0,1,0,0,0,1
+Calahonda apartments - Los Jarales,Mijas Costa,7.5,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",247.699,Mijas Costa,Spain,0,1,0,0,0,0,0,2
+Hotel 280 Zaragoza Inspired by B&B HOTELS,Calatorao,7.7,8.3,Twin Room,2 twin beds,146.69400000000002,Calatorao,Spain,2,0,0,0,0,0,0,2
+Finca el Castillo,Buenavista del Norte,8.9,8.7,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",192.993,Buenavista del Norte,Spain,0,1,0,0,0,0,0,2
+Alexandre Hotel Troya,Playa de las Americas,7.7,8.0,Family Room (2 Adults + 2 Children),   1 double or 2 twins,599.194,Playa de las Americas,Spain,2,0,0,0,0,0,0,2
+Can Barnosell - Els Masos d'en Coll,Llaviá,9.0,9.2,Superior Double Room,1 full bed,274.892,Llaviá,Spain,0,1,0,0,0,0,0,1
+Poniente Playa Aparthotel,San Antonio,8.2,8.5,One-Bedroom Apartment with Air Conditioning,"4 beds (2 twins, 2 sofa beds)",376.082,San Antonio,Spain,2,0,0,0,2,0,0,4
+Casa Rural Las Albertas,Arbancón,8.9,9.1,Deluxe Suite with Spa Bath,"3 beds (2 twins, 1 full)",354.249,Arbancón,Spain,2,1,0,0,0,0,0,3
+RH Don Carlos & Spa,"Norte Beach, Peniscola",8.7,9.0,Twin Room,2 twin beds,199.905,Peniscola,Spain,2,0,0,0,0,0,0,2
+Casthotels Fuertesol Bungalows,Caleta De Fuste,7.1,0.0,One-Bedroom Apartment,2 twin beds,129.149,Caleta De Fuste,Spain,2,0,0,0,0,0,0,2
+Lua Hotel Boutique,El Pinar del Hierro,8.4,8.5,Standard Double Room,   1 double or 2 twins,174.752,El Pinar del Hierro,Spain,2,0,0,0,0,0,0,2
+Hostal Trainera,Esterri d'Àneu,8.0,8.0,Triple Room with Spa Access,3 twin beds,219.31,Esterri d'Àneu,Spain,3,0,0,0,0,0,0,3
+Design Plus Bex Hotel,Las Palmas de Gran Canaria,8.2,8.7,Deluxe Twin Room (2 Adults),2 twin beds,213.74,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Apartaments Rural Montseny,gualba de Dalt,8.6,8.6,Three-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 bunk bed)",263.172,Gualba,Espagne,2,1,0,0,0,0,0,4
+Lindner Golf Resort Portals Nous,Portals Nous,8.6,9.0,   Class Room Pool Side (2 Adults),Multiple bed types,814.6850000000001,Portals Nous,Spain,0,0,0,0,0,0,0,0
+Silken Saaj Las Palmas,Las Palmas de Gran Canaria,8.3,8.8,Standard Double or Twin Room,2 twin beds,221.631,Las Palmas,Spain,2,0,0,0,0,0,0,2
+La Posada Cercedilla,Cercedilla,8.5,8.4,Double Room,1 full bed,261.78000000000003,Cercedilla,Spain,0,1,0,0,0,0,0,1
+Veragua. Glamping y Apartamentos turísticos,Villanueva de la Vera,9.1,9.1,Tent (5 Adults),"3 beds (1 twin, 1 full, 1 bunk bed)",278.48900000000003,Villanueva de la Vera,Spain,1,1,0,0,0,0,0,3
+Apartamento Camiño dos Faros,Ponteceso,9.0,9.0,Two-Bedroom Apartment,"3 beds (1 twin, 1 full, 1 sofa bed)",143.095,Ponteceso,Spain,1,1,0,0,0,0,0,3
+Aparthotel Ona Campanario,La Cala de Mijas,8.5,8.6,Three-Bedroom Apartment (8 Adults),"6 beds (4 twins, 1 full, 1 sofa bed)",311.357,La Cala de Mijas,Spain,4,1,0,0,0,0,0,6
+Hotel Meleiros,Castro de Sanabria,7.8,8.0,Twin Room,2 twin beds,170.57500000000002,Castro de Sanabria,Spain,2,0,0,0,0,0,0,2
+Amazing Duna Beach Seafront Apartament,Torrox,9.7,9.8,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",343.586,Torrox,Spain,2,1,0,0,0,0,0,4
+Apartamentos El Bergantin Menorca Club,Fornells,9.0,9.3,Studio with Sea View - Adults Only,1 full bed,245.627,Fornells,Spain,0,1,0,0,0,0,0,1
+Apartamentos Galicia Norte,O Porto de Espasante,7.9,8.2,Deluxe Junior Suite,"3 beds (2 twins, 1 full)",316.781,Espasante,Spain,2,1,0,0,0,0,0,3
+BelleVue Club,Port d'Alcudia,6.7,0.0,Studio,"3 beds (2 twins, 1 sofa bed)",138.187,Port d'Alcudia,Spain,2,0,0,0,0,0,0,3
+Hotel Santuari,Balaguer,8.4,8.9,Twin Room,2 twin beds,183.107,Balaguer,Spain,2,0,0,0,0,0,0,2
+Apartamentos Solamaza,Isla,8.4,8.6,Apartment with Terrace (4-6 Adults),"4 beds (2 twins, 1 full, 1 sofa bed)",353.623,Isla,Spain,2,1,0,0,0,0,0,4
+Hotel Convent de Begur,Begur,8.6,9.0,Small Double Room,1 full bed,327.461,Begur,Spain,0,1,0,0,0,0,0,1
+Posada Miranda,Miranda del Castañar,8.5,8.8,Double or Twin Room,   1 double or 2 twins,212.348,Miranda del Castañar,Spain,2,0,0,0,0,0,0,2
+RK Farallón Canteras,Las Palmas de Gran Canaria,8.8,9.1,Standard Apartment (King Bed),1 queen bed,194.362,Las Palmas,Spain,0,0,1,0,0,0,0,1
+Exe Plaza,"Tetuan, Madrid",8.4,8.8,Double or Twin Room,   1 double or 2 twins,500.35200000000003,Madrid,Spain,2,0,0,0,0,0,0,2
+Casa Gabriel,Vecindario,9.1,9.1,Three-Bedroom Apartment,"5 beds (2 twins, 1 full, 2 bunk beds)",182.75900000000001,Vecindario,Spain,2,1,0,0,0,2,0,5
+Hotel & Spa Sierra de Cazorla 4*,Cazorla,8.3,8.6,Twin Room,2 twin beds,267.93,Cazorla,Spain,2,0,0,0,0,0,0,2
+The Social Hub Madrid,"Moncloa-Aravaca, Madrid",8.8,9.2,Standard King Room,1 king bed,517.874,Madrid,Spain,0,0,0,1,0,0,0,1
+Hotel Apartamentos Loto Conil,Conil de la Frontera,9.0,9.3,Apartment with Pool View,"2 beds (1 full, 1 sofa bed)",237.839,Conil de la Frontera,Spain,0,1,0,0,0,0,0,2
+Hotel Delfín,Tossa de Mar,8.9,9.4,Double or Twin Room with Terrace,Multiple bed types,215.133,Tossa de Mar,Spain,0,0,0,0,0,0,0,0
+Villa June,Benitachell,8.0,0.0,Villa with Private Pool,"3 beds (2 twins, 1 full)",358.207,Benitachell,Spain,2,1,0,0,0,0,0,3
+La Posada del Casar,Casar de Palomero,9.1,9.4,Double or Twin Room,   1 double or 2 twins,236.136,Casar de Palomero,Spain,2,0,0,0,0,0,0,2
+Vincci Frontaura,Valladolid,8.5,9.0,Standard Double or Twin Room,   1 double or 2 twins,287.766,Valladolid,Spain,2,0,0,0,0,0,0,2
+Motel Emporio,Cabezón de Pisuerga,7.9,8.3,Double Room,1 queen bed,121.259,Cabezón de Pisuerga,Spain,0,0,1,0,0,0,0,1
+Irenaz Resort Apartamentos,San Sebastián,8.8,9.1,One-Bedroom Apartment with Terrace,"2 beds (1 sofa bed, 1 queen)",518.859,San Sebastián,Spain,0,0,1,0,0,0,0,2
+Hostal Casa Juan,Castejón de Sos,8.2,8.1,Twin Room,2 twin beds,130.751,Castejón de Sos,Spain,2,0,0,0,0,0,0,2
+Hotel Elorrio,Elorrio,8.4,8.9,Double Room,1 full bed,201.78900000000002,Elorrio,Spain,0,1,0,0,0,0,0,1
+Hotel Intriago I & II,Intriago,8.0,0.0,Standard Double or Twin Room,   1 double or 2 twins,146.787,Intriago,Spain,2,0,0,0,0,0,0,2
+Tierra de Biescas,Biescas,8.7,9.0,Double or Twin Room with Cycling Package,   1 double or 2 twins,302.254,Biescas,Spain,2,0,0,0,0,0,0,2
+Valle Del Este Golf Resort,Vera,8.3,8.6,Twin Room with Golf View,2 twin beds,308.636,Vera,Spain,2,0,0,0,0,0,0,2
+Barceló Margaritas,Playa del Ingles,8.3,8.8,Double Room,"3 beds (2 twins, 1 sofa bed)",292.065,Playa del Ingles,Spain,2,0,0,0,0,0,0,3
+Hostal Al-Qazeres,Cáceres,7.4,0.0,Economy Double Room,1 full bed,145.046,Cáceres,Spain,0,1,0,0,0,0,0,1
+NYX Hotel Madrid by Leonardo Hotels,"Tetuan, Madrid",8.2,8.7,Economy Double Room,1 full bed,524.604,Madrid,Spain,0,1,0,0,0,0,0,1
+Apartamentos Los Tarajales,Valle Gran Rey,7.9,8.1,Studio with Sea View,2 twin beds,194.942,Valle Gran Rey,Spain,2,0,0,0,0,0,0,2
+Casa Telma,Alcañiz,9.4,9.3,Bungalow,"3 beds (2 twins, 1 sofa bed)",254.121,Alcañiz,Spain,2,0,0,0,0,0,0,3
+Hostal San Isidro,Autol,7.6,0.0,Deluxe Queen Room,1 full bed,119.402,Autol,Spain,0,1,0,0,0,0,0,1
+Hotel Arnal,Escalona,8.7,8.9,Triple Room,3 twin beds,201.673,Escalona,Spain,3,0,0,0,0,0,0,3
+Casas Maria Carmona,El Pozo de los Frailes,8.7,8.8,Two-Bedroom Apartment - Casa Luna,"3 beds (2 twins, 1 full)",163.90200000000002,El Pozo de los Frailes,Spain,2,1,0,0,0,0,0,3
+Eurostars Reina Felicia,Jaca,8.4,9.0,Standard Twin Room,   1 double or 2 twins,205.966,Jaca,Spain,2,0,0,0,0,0,0,2
+Camping Roca Grossa,Calella,8.5,8.4,Two-Bedroom Bungalow (4 Adults),"2 beds (1 full, 1 bunk bed)",211.768,Calella,Spain,0,1,0,0,0,0,0,2
+Hotel Arillo,Noja,7.2,0.0,Double or Twin Room,   1 double or 2 twins,185.659,Noja,Spain,2,0,0,0,0,0,0,2
+Hostal Isabel II,Figueres,7.2,0.0,Double or Twin Room,   1 double or 2 twins,113.327,Figueres,Spain,2,0,0,0,0,0,0,2
+Ona Marinas de Nerja Spa Resort,Nerja,8.5,8.8,One-Bedroom Apartment with Terrace (2 Adults),2 twin beds,461.712,Nerja,Spain,2,0,0,0,0,0,0,2
+Hotel La Posada,Broto,7.7,8.0,Economy Double or Twin Room,   1 double or 2 twins,117.19800000000001,Broto,Spain,2,0,0,0,0,0,0,2
+Exe Sevilla Macarena,"Macarena, Seville",7.5,8.0,Double or Twin Room,2 twin beds,418.66200000000003,Seville,Spain,2,0,0,0,0,0,0,2
+VIK Coral Beach,Playa Blanca,7.7,0.0,Two- Bedroom Villa with Sea View,"4 beds (2 twins, 1 full, 1 sofa bed)",339.098,Playa Blanca,Spain,2,1,0,0,0,0,0,4
+Senator Barajas,"Barajas, Madrid",7.7,8.2,Double King Room,1 queen bed,294.361,Madrid,Spain,0,0,1,0,0,0,0,1
+Hotel Rural La Muedra,Vinuesa,8.4,8.5,Queen Suite with Spa Bath,1 king bed,438.307,Vinuesa,Spain,0,0,0,1,0,0,0,1
+NH Marbella,Marbella,8.3,8.8,Standard Double or Twin Room with Terrace,Multiple bed types,400.681,Marbella,Spain,0,0,0,0,0,0,0,0
+Hotel Vila-real Palace,Villareal,7.4,0.0,Double or Twin Room,2 twin beds,217.372,Villareal,Spain,2,0,0,0,0,0,0,2
+Hotel Reymar Playa,"Malgrat de Mar Beach, Malgrat de Mar",7.6,8.0,Family Room,4 twin beds,206.546,Malgrat de Mar,Spain,4,0,0,0,0,0,0,4
+Estella by Vive La Exclusividad,Güimar,0.0,0.0,Two-Bedroom Apartment,"3 beds (2 twins, 1 queen)",97.764,Güimar,Spain,2,0,1,0,0,0,0,3
+Royal Golf Park Club,San Miguel de Abona,8.7,8.8,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",207.126,San Miguel de Abona,Spain,2,0,0,0,0,0,0,3
+ROCASOL - Beach,"Fossa-Levante Beach, Calpe",8.9,9.2,One-Bedroom Apartment,1 full bed,182.75900000000001,Calpe,Spain,0,1,0,0,0,0,0,1
+AxelBeach Maspalomas - Apartments and Lounge Club - Adults Only,Playa del Ingles,7.9,8.2,One-Bedroom Apartment (2 Adults),"2 beds (1 sofa bed, 1 queen)",222.809,Playa del Ingles,Spain,0,0,1,0,0,0,0,2
+Apartamento Postman,Arenales del Sol,9.8,9.8,Apartment,"3 beds (1 twin, 1 bunk bed, 1 queen)",515.205,Arenales del Sol,Spain,1,0,1,0,0,0,0,3
+Hipotels Dunas Aparthotel,Cala Millor,8.5,8.6,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",248.705,Cala Millor,Spain,2,0,0,0,0,0,0,3
+Apartamentos Miguel Angel,Estepona,9.2,9.4,One-Bedroom Apartment (2 Adults),"3 beds (2 twins, 1 full)",190.06900000000002,Estepona,Spain,2,1,0,0,0,0,0,3
+TWO Hotel Barcelona by Axel 4* Sup- Adults Only,"Eixample, Barcelona",8.5,9.0,Double or Twin Room,1 queen bed,639.03,Barcelona,Spain,0,0,1,0,0,0,0,1
+Eurostars Plaza Acueducto,Segovia,8.4,8.9,Double or Twin Room (1-2 Adults),   1 double or 2 twins,309.819,Segovia,Spain,2,0,0,0,0,0,0,2
+Hotel Alfonso I,Tui,8.3,8.5,Suite,1 king bed,307.034,Tui,Spain,0,0,0,1,0,0,0,1
+Vista Mazarron,Puerto de Mazarrón,8.5,0.0,Three-Bedroom House,"7 beds (2 twins, 1 full, 2 bunk beds, 2 sofa beds)",217.11700000000002,Puerto de Mazarrón,Spain,2,1,0,0,2,2,0,7
+Piso muy Grande,Moaña,8.8,8.9,Apartment,"6 beds (4 twins, 2 fulls)",219.31,Moaña,Spain,4,2,0,0,0,0,0,6
+Casa Guaranatura alojamiento y barrancos,Bierge,7.0,0.0,Double Room with Shared Bathroom,1 full bed,170.57500000000002,Bierge,Spain,0,1,0,0,0,0,0,1
+Best Club Vacaciones Pueblo Indalo,Mojácar,8.0,0.0,Two-Bedroom Apartment (5 Adults),"5 beds (4 twins, 1 sofa bed)",202.616,Mojácar,Spain,4,0,0,0,0,0,0,5
+Estudio Edificio Salvia zona remontes,Sierra Nevada,7.5,0.0,One-Bedroom Apartment,"3 beds (2 bunk beds, 1 sofa bed)",223.696,Sierra Nevada,Spain,0,0,0,0,0,2,0,3
+Hotel Harrison Etxea,Amorebieta-Etxano,7.1,0.0,Standard Twin Room,2 twin beds,157.23,Amorebieta-Etxano,Spain,2,0,0,0,0,0,0,2
+Hostal Restaurante La Curva,Adra,6.4,0.0,Twin Room,2 twin beds,146.787,Adra,Spain,2,0,0,0,0,0,0,2
+camping yaso-guara,Yaso,7.4,0.0,Tent,2 twin beds,48.736000000000004,Yaso,Spain,2,0,0,0,0,0,0,2
+Lovely double room with private bathroom and pool,Albox,7.7,9.2,Double Room,1 full bed,92.598,Albox,Spain,0,1,0,0,0,0,0,1
+Puerto Antilla Grand Hotel,Islantilla,8.9,9.1,Twin Room (2 Adults),2 twin beds,244.838,Islantilla,Spain,2,0,0,0,0,0,0,2
+Elba Lucía Sport & Suite Hotel,Costa de Antigua,7.3,0.0,One-Bedroom Apartment (2 Adults),2 twin beds,154.631,Costa de Antigua,Spain,2,0,0,0,0,0,0,2
+Hotel Kaktus Albir,Albir,8.0,8.3,Junior Suite with Sea View,2 twin beds,680.644,Albir,Spain,2,0,0,0,0,0,0,2
+Petit Palace Tamarises,Getxo,8.2,8.5,Superior Room with Balcony,"2 beds (1 full, 1 bunk bed)",391.161,Getxo,Spain,0,1,0,0,0,0,0,2
+Natura Resorts,Casalarreina,8.9,9.4,Superior Villa,"4 beds (2 twins, 1 sofa bed, 1 queen)",368.52500000000003,Casalarreina,Spain,2,0,1,0,0,0,0,4
+Andamur San Román,San Román,8.3,8.5,Double Room,1 full bed,163.96,San Román,Spain,0,1,0,0,0,0,0,1
+Exe Zizur Pamplona,Zizur Mayor,8.4,8.8,Double or Twin Room,   1 double or 2 twins,201.905,Zizur Mayor,Spain,2,0,0,0,0,0,0,2
+Hotel Ereza Mar - Adults Only,Caleta De Fuste,8.2,8.9,Double or Twin Room,   1 double or 2 twins,386.404,Caleta De Fuste,Spain,2,0,0,0,0,0,0,2
+Catalonia del Mar - Adults Only,Cala Bona,8.2,8.5,Double or Twin Room with Pool View,   1 double or 2 twins,200.512,Son Servera,Espagne,2,0,0,0,0,0,0,2
+Hotel & Spa Real Villa Anayet,Canfranc-Estación,8.4,8.8,Double or Twin Room with View,   1 double or 2 twins,250.64000000000001,Canfranc-Estación,Spain,2,0,0,0,0,0,0,2
+KiCo,Cómpeta,9.4,9.5,Twin Room with Private Bathroom,2 twin beds,165.933,Cómpeta,Spain,2,0,0,0,0,0,0,2
+SALOU 4 YOU apartamentos,Salou,7.2,0.0,Studio with Sofa Bed,"2 beds (1 full, 1 sofa bed)",171.27100000000002,Salou,Spain,0,1,0,0,0,0,0,2
+Pola Giverola Resort,Tossa de Mar,8.2,8.3,Open Suite with Kitchen and Sea View,"4 beds (2 twins, 2 sofa beds)",410.925,Tossa de Mar,Spain,2,0,0,0,2,0,0,4
+Hotel Los Angeles,La Bañeza,7.0,0.0,Twin Room with Private Bathroom,2 twin beds,134.023,La Bañeza,Spain,2,0,0,0,0,0,0,2
+Sono & SPA - Adults Only,La Garriga,8.9,9.2,Superior Studio with Kitchen with Private Spa Access,Multiple bed types,255.862,La Garriga,Spain,0,0,0,0,0,0,0,0
+Hesperia Córdoba,Córdoba,8.4,8.9,Standard Double or Twin Room with View,Multiple bed types,523.511,Córdoba,Spain,0,0,0,0,0,0,0,0
+Apartaments Cal Noi,Camprodon,8.7,8.8,One-Bedroom Superior Apartment (2 Adults + 1 Child),"3 beds (2 twins, 1 sofa bed)",203.52,Camprodon,Spain,2,0,0,0,0,0,0,3
+La Finca Uga,Uga,9.2,8.9,Studio with Park View,1 full bed,162.133,Uga,Spain,0,1,0,0,0,0,0,1
+Lopesan Baobab Resort,Meloneras,8.7,9.2,Senior Suite (2 Adults),1 king bed,1446.474,Meloneras,Spain,0,0,0,1,0,0,0,1
+Apartamentos La Laguna II Luxury Apartments,Ciudad Quesada,8.7,9.4,Apartment,"3 beds (2 twins, 1 king)",321.446,Ciudad Quesada,Spain,2,0,0,1,0,0,0,3
+Hotel Norte y Londres,Burgos,7.9,8.1,Double or Twin Room,   1 double or 2 twins,215.342,Burgos,Spain,2,0,0,0,0,0,0,2
+Rafaelhoteles Badalona,Badalona,8.0,8.5,Double or Twin Room (1-2 Adults),   1 double or 2 twins,381.391,Badalona,Spain,2,0,0,0,0,0,0,2
+"Santa Catalina, a Royal Hideaway Hotel",Las Palmas de Gran Canaria,9.1,9.5,Deluxe Double Room,   1 double or 2 twins,447.39300000000003,Las Palmas,Spain,2,0,0,0,0,0,0,2
+Apartamentos Jet - Adults Only,Playa d'en Bossa,8.1,8.0,Two-Bedroom Suite with Sea View,4 twin beds,543.953,Playa d'en Bossa,Spain,4,0,0,0,0,0,0,4
+Altamira Camping Park,Queveda,7.6,0.0,One-Bedroom Bungalow (2 Adults),1 full bed,175.229,Queveda,Spain,0,1,0,0,0,0,0,1
+AC Hotel Iberia Las Palmas by Marriott,Las Palmas de Gran Canaria,7.8,8.2,"Standard King, Guest room, 1 King",1 king bed,203.686,Las Palmas,Spain,0,0,0,1,0,0,0,1
+Hotel d'Or,Cala d´Or,8.2,8.4,Twin Room,2 twin beds,183.928,Santanyí,Espagne,2,0,0,0,0,0,0,2
+Hostal Meseguer,El Alted,7.8,8.0,Twin Room,2 twin beds,134.719,El Alted,Spain,2,0,0,0,0,0,0,2
+Novotel Campo De Las Naciones,"Barajas, Madrid",8.4,8.8,Deluxe Room with Double Bed,1 full bed,493.39,Madrid,Spain,0,1,0,0,0,0,0,1
+AC Hotel Tarragona by Marriott,Tarragona,8.4,8.8,"Standard Room, Guest room, 1 Queen",1 queen bed,247.911,Tarragona,Spain,0,0,1,0,0,0,0,1
+Senator Banus Spa Hotel,Estepona,7.8,8.3,Suite with Terrace,"3 beds (2 twins, 1 queen)",509.699,Estepona,Spain,2,0,1,0,0,0,0,3
+Berga Resort,Berga,8.7,8.9,Bungalow,1 full bed,246.579,Berga,Spain,0,1,0,0,0,0,0,1
+Camping Bayona Playa,Sabaris,8.7,8.7,Tent,2 twin beds,95.73100000000001,Sabaris,Spain,2,0,0,0,0,0,0,2
+La Senda de los Enebros,Higuera de las Dueñas,9.4,9.4,Standard Double or Twin Room,   1 double or 2 twins,207.126,Higuera de las Dueñas,Spain,2,0,0,0,0,0,0,2
+Hesperia Bilbao,"Bilbao City Centre, Bilbao",8.5,9.0,Standard Double or Twin Room,   1 double or 2 twins,360.587,Bilbao,Spain,2,0,0,0,0,0,0,2
+Exe Guadalete,Jerez de la Frontera,8.0,8.4,Double or Twin Room,2 twin beds,471.575,Jerez de la Frontera,Spain,2,0,0,0,0,0,0,2
+SBH Club Paraiso Playa,Playa Jandia,7.6,0.0,Standard Double Room,2 twin beds,301.416,Playa Jandia,Spain,2,0,0,0,0,0,0,2
+Hostal Alba,La Mata,8.2,8.2,Two-Bedroom Apartment (3 Adults +3 Children),"2 beds (1 full, 1 bunk bed)",263.172,La Mata,Spain,0,1,0,0,0,0,0,2
+Hacienda La Dehesa (Only Adults),Buenavista del Norte,9.0,9.0,Superior Apartment,"3 beds (2 twins, 1 queen)",515.205,Buenavista del Norte,Spain,2,0,1,0,0,0,0,3
+Meliá Cala Galdana,Cala Galdana,8.4,8.9,Melia Guestroom Cala Galdana View,1 king bed,453.821,Cala Galdana,Spain,0,0,0,1,0,0,0,1
+Arrels d'Emporda,Palafrugell,8.5,8.8,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",232.136,Palafrugell,Spain,0,1,0,0,0,0,0,2
+Hotel Veracruz Plaza & Spa,Valdepeñas,8.6,9.0,Superior Double Room,1 king bed,303.553,Valdepeñas,Spain,0,0,0,1,0,0,0,1
+Hotel Trias,Palamós,8.4,8.8,Twin Room with Street View,2 twin beds,236.495,Palamós,Spain,2,0,0,0,0,0,0,2
+Zoku Paris,"17th arr., Paris",9.2,9.5,Loft,1 king bed,337.341,Paris,France,0,0,0,1,0,0,0,1
+Paris - Quartier Latin - 30 m2,"5th arr., Paris",0.0,0.0,Double Room,"2 beds (1 full, 1 queen)",455.678,Paris,France,0,1,1,0,0,0,0,2
+Grand Hotel Francais,"11th arr., Paris",8.5,8.8,Superior Room,1 full bed,586.916,Paris,France,0,1,0,0,0,0,0,1
+Luxury apartment in Paris in a chic area,"15th arr., Paris",0.0,0.0,Deluxe Apartment,"6 beds (3 fulls, 2 sofa beds, 1 queen)",792.246,Paris,France,0,3,1,0,2,0,0,6
+Paris Roquette,"11th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",346.197,Paris,France,0,1,0,0,0,0,0,2
+Sublim Loft in Paris/Père Lachaise,Paris,0.0,0.0,Three-Bedroom Apartment,"4 beds (2 fulls, 1 sofa bed, 1 queen)",950.072,Paris,France,0,2,1,0,0,0,0,4
+Appartement dans le 11e arrondissement,Paris,0.0,0.0,One-Bedroom Apartment,1 full bed,310.689,Paris,France,0,1,0,0,0,0,0,1
+Appart'City Classic Paris La Villette,"19th arr., Paris",7.1,0.0,Double Studio,1 full bed,305.526,Paris,France,0,1,0,0,0,0,0,1
+Hotel Riviera,"9th arr., Paris",5.2,0.0,Double Room,1 full bed,410.319,Paris,France,0,1,0,0,0,0,0,1
+Emerald Oasis close to Fashionable Le Marais,"3rd arr., Paris",3.0,0.0,Studio,1 full bed,403.809,Paris,France,0,1,0,0,0,0,0,1
+Hôtel Mistral,"14th arr., Paris",8.7,9.0,   Double Room,1 full bed,572.295,Paris,France,0,1,0,0,0,0,0,1
+Hôtel Lebron,"9th arr., Paris",5.5,0.0,Double Room,1 full bed,337.668,Paris,France,0,1,0,0,0,0,0,1
+Hotel Richmond Gare du Nord,"10th arr., Paris",7.0,0.0,Triple Room,"2 beds (1 twin, 1 full)",638.775,Paris,France,1,1,0,0,0,0,0,2
+Hôtel Renoir Montparnasse,"14th arr., Paris",6.2,0.0,Double or Twin Room,   1 double or 2 twins,410.075,Paris,France,2,0,0,0,0,0,0,2
+Hotel Royal Phare,"7th arr., Paris",8.3,8.7,Classic Double Room,1 queen bed,689.963,Paris,France,0,0,1,0,0,0,0,1
+"Paris proche champs Elysée, superbe appartement.",Paris,0.0,0.0,Two-Bedroom Apartment,"3 beds (1 twin, 1 king, 1 queen)",1206.2060000000001,Paris,France,1,0,1,1,0,0,0,3
+Aparthotel Adagio Porte de Versailles,Paris,7.6,0.0,Studio with Balcony,   1 double or 2 twins,524.372,Paris,France,2,0,0,0,0,0,0,2
+Hôtel Du Leman,"9th arr., Paris",7.4,0.0,Double Room,1 full bed,409.379,Paris,France,0,1,0,0,0,0,0,1
+Bright apartment for 2 people - Paris 15,"15th arr., Paris",0.0,0.0,Apartment,1 full bed,312.124,Paris,France,0,1,0,0,0,0,0,1
+La Belle Ville,"19th arr., Paris",8.8,9.3,Superior Double Room,1 full bed,563.128,Paris,France,0,1,0,0,0,0,0,1
+Superbe appartement au cœur de Paris - Marais,"1st arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (1 twin, 1 full, 1 sofa bed)",1206.786,Paris,France,1,1,0,0,0,0,0,3
+Ibis Paris Tour Eiffel Cambronne 15ème,"15th arr., Paris",7.5,0.0,Standard Room with 2 Single Beds,2 twin beds,483.77,Paris,France,2,0,0,0,0,0,0,2
+Austin's Arts Et Metiers Hotel,"3rd arr., Paris",8.1,8.6,Double Room,1 full bed,676.847,Paris,France,0,1,0,0,0,0,0,1
+Superbe Appartement - Hyper Centre - Romantique,"2nd arr., Paris",7.0,0.0,One-Bedroom Apartment,1 full bed,665.821,Paris,France,0,1,0,0,0,0,0,1
+Hotel de France 18,"18th arr., Paris",5.9,0.0,Double Room with Shared Bathroom,1 full bed,214.57500000000002,Paris,France,0,1,0,0,0,0,0,1
+Luxurious Apartment next to Arc de Triomphe,"17th arr., Paris",10.0,10.0,One-Bedroom Apartment,1 full bed,974.712,Paris,France,0,1,0,0,0,0,0,1
+Courcelles Médéric,"17th arr., Paris",7.8,8.3,Superior Double Room,1 full bed,673.248,Paris,France,0,1,0,0,0,0,0,1
+Hôtel du Parc Montparnasse,"14th arr., Paris",8.0,8.5,Standard Twin Room,2 twin beds,508.127,Paris,France,2,0,0,0,0,0,0,2
+New Hôtel Gare Du Nord,"10th arr., Paris",7.1,0.0,Double Room,1 full bed,484.38300000000004,Paris,France,0,1,0,0,0,0,0,1
+Hôtel George Washington,"8th arr., Paris",7.1,0.0,Classique Double Room,1 full bed,938.543,Paris,France,0,1,0,0,0,0,0,1
+Residence Élysées,"8th arr., Paris",5.6,0.0,One-Bedroom Apartment,"3 beds (2 sofa beds, 1 queen)",695.94,Paris,France,0,0,1,0,2,0,0,3
+Appartement Glaciere,"13th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",523.908,Paris,France,0,1,0,0,0,0,0,2
+Hôtel De Castiglione,"8th arr., Paris",7.5,8.0,Superior Double Room,Multiple bed types,676.497,Paris,France,0,0,0,0,0,0,0,0
+My Maison In Paris Invalides,"7th arr., Paris",9.2,9.4,Deluxe Studio,1 queen bed,886.8720000000001,Paris,France,0,0,1,0,0,0,0,1
+Nice and calm studio nearby Alesia at the heart of Paris - Welkeys,"14th arr., Paris",5.7,0.0,Studio,1 king bed,264.689,Paris,France,0,0,0,1,0,0,0,1
+Mama Shelter Paris East,"20th arr., Paris",8.0,8.5,Large Mama Double Room,1 full bed,458.231,Paris,France,0,1,0,0,0,0,0,1
+Hotel Opéra Marigny,"8th arr., Paris",8.1,8.6,Classic Double Room,   1 double or 2 twins,704.299,Paris,France,2,0,0,0,0,0,0,2
+Elysées Ceramic,"8th arr., Paris",8.3,8.6,Double Room,1 queen bed,619.058,Paris,France,0,0,1,0,0,0,0,1
+Appartement vue Tour Effiel 6P,"16th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (1 full, 1 sofa bed, 1 queen)",1726.053,Paris,France,0,1,1,0,0,0,0,3
+Hôtel de l'Exposition - République,"10th arr., Paris",6.6,0.0,Double Room,1 full bed,481.438,Paris,France,0,1,0,0,0,0,0,1
+Hôtel du Temps,"9th arr., Paris",8.4,8.8,   Double Room,1 queen bed,697.383,Paris,France,0,0,1,0,0,0,0,1
+Victory Hotel Galou,"10th arr., Paris",6.0,0.0,Standard Triple Room,"2 beds (1 twin, 1 full)",564.753,Paris,France,1,1,0,0,0,0,0,2
+Renaissance Paris Vendome Hotel,"1st arr., Paris",7.6,8.1,Studio Queen Room,1 queen bed,1553.865,Paris,France,0,0,1,0,0,0,0,1
+Hotel Archetype Etoile,"17th arr., Paris",8.6,9.0,Double Room,Multiple bed types,605.714,Paris,France,0,0,0,0,0,0,0,0
+Maison de la joie,"14th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",994.206,Paris,France,0,1,0,0,0,0,0,2
+Hotel Novex Paris,"13th arr., Paris",4.6,0.0,Double Room,1 full bed,277.08,Paris,France,0,1,0,0,0,0,0,1
+Suites & Hôtel Helzear Etoile,"8th arr., Paris",8.3,8.7,Junior Suite,1 queen bed,784.2950000000001,Paris,France,0,0,1,0,0,0,0,1
+Maison Delano Paris,"8th arr., Paris",0.0,0.0,Deluxe Double Room,1 full bed,1990.037,Paris,France,0,1,0,0,0,0,0,1
+  able townhouse in the heart of Paris,"15th arr., Paris",5.9,0.0,Studio,1 sofa bed,267.527,Paris,France,0,0,0,0,0,0,0,0
+Hôtel Apollo Opéra,"9th arr., Paris",6.9,0.0,Double Room,1 full bed,397.562,Paris,France,0,1,0,0,0,0,0,1
+Castille Paris – Starhotels Collezione,"1st arr., Paris",8.3,8.7,Superior Room,1 full bed,1375.5620000000001,Paris,France,0,1,0,0,0,0,0,1
+Paradise Crib,"15th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 bunk bed, 1 sofa bed)",353.333,Paris,France,0,0,0,0,0,0,0,2
+Unic Renoir Saint Germain,"14th arr., Paris",6.5,0.0,Double Room or Twin Room,1 full bed,410.075,Paris,France,0,1,0,0,0,0,0,1
+Hôtel du Maine,"14th arr., Paris",7.4,0.0,Superior Room,   1 double or 2 twins,556.63,Paris,France,2,0,0,0,0,0,0,2
+BEAUGRENELLE TOUR EIFFEL PARIS,"15th arr., Paris",5.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",509.28700000000003,Paris,France,0,1,0,0,0,0,0,2
+Hôtel Ambre,"13th arr., Paris",7.6,0.0,Double Room,1 full bed,450.108,Paris,France,0,1,0,0,0,0,0,1
+Studio meublé Place de la Nation,"11th arr., Paris",4.4,0.0,Family Studio,"2 beds (1 bunk bed, 1 sofa bed)",280.23,Paris,France,0,0,0,0,0,0,0,2
+GuestReady -Beautiful 2-Room in the Heart of Paris,"12th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"2 beds (1 twin, 1 full)",654.568,Paris,France,1,1,0,0,0,0,0,2
+Aparthotel Adagio Paris XV,"15th arr., Paris",6.8,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",486.196,Paris,France,0,1,0,0,0,0,0,2
+Hôtel de France Quartier Latin,"5th arr., Paris",7.9,0.0,Double Room,1 full bed,418.047,Paris,France,0,1,0,0,0,0,0,1
+Sweet and cosy appartement,"15th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,462.988,Paris,France,0,1,0,0,0,0,0,1
+HOTEL AU COEUR DES ARTS ET METIERS,"3rd arr., Paris",8.0,8.6,Double Room with Courtyard View,1 full bed,592.718,Paris,France,0,1,0,0,0,0,0,1
+Apartments WS Champs Elysées - Ponthieu,"8th arr., Paris",7.0,0.0,Studio Apartment,1 full bed,585.872,Paris,France,0,1,0,0,0,0,0,1
+Hôtel Barrière Fouquet's Paris,"8th arr., Paris",9.0,9.5,Superior Room,Multiple bed types,2584.936,Paris,France,0,0,0,0,0,0,0,0
+Apartments WS Marais - Musée Pompidou,"3rd arr., Paris",7.2,8.1,Standard Studio,1 full bed,613.964,Paris,France,0,1,0,0,0,0,0,1
+Villa Panthéon,"5th arr., Paris",7.7,8.2,Classic Room,   1 double or 2 twins,855.759,Paris,France,2,0,0,0,0,0,0,2
+Studio Île Saint Louis 75004 Paris,"4th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,646.965,Paris,France,0,1,0,0,0,0,0,1
+Hotel Saint Dominique,"7th arr., Paris",8.3,8.8,Superior Double Room,1 queen bed,906.946,Paris,France,0,0,1,0,0,0,0,1
+"Prince de Galles, a Luxury Collection hotel, Paris","8th arr., Paris",8.6,9.0,"Art Deco Queen, Guest room, 1 Queen",1 queen bed,2744.2780000000002,Paris,France,0,0,1,0,0,0,0,1
+Joli 2 pièces loft place Vendôme/ Champs Elysées,"1st arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",1560.2350000000001,Paris,France,0,1,0,0,0,0,0,2
+Montmartre Residence,"18th arr., Paris",9.1,9.4,Deluxe One-Bedroom Apartment (4 people),"2 beds (1 sofa bed, 1 queen)",1240.367,Paris,France,0,0,1,0,0,0,0,2
+Pratic Hotel,"4th arr., Paris",7.4,8.0,Standard Double Room,1 full bed,490.48900000000003,Paris,France,0,1,0,0,0,0,0,1
+Amazing apartment-2P-Sacré Coeur-Poissonniers-2,"18th arr., Paris",8.3,0.0,Studio,1 full bed,289.701,Paris,France,0,1,0,0,0,0,0,1
+1K Paris,"3rd arr., Paris",7.6,8.1,Deluxe L Double Room,Multiple bed types,813.056,Paris,France,0,0,0,0,0,0,0,0
+Drawing Hotel,"1st arr., Paris",8.7,9.3,Superior Double Room,   1 double or 2 twins,1143.314,Paris,France,2,0,0,0,0,0,0,2
+High Standing Apartment with Sauna,"16th arr., Paris",0.0,0.0,Superior Apartment with Sauna,"5 beds (4 fulls, 1 sofa bed)",629.664,Paris,France,0,4,0,0,0,0,0,5
+Hôtel de la Bourdonnais,"7th arr., Paris",8.6,9.0,Deluxe Triple Room,"2 beds (1 sofa bed, 1 queen)",1314.03,Paris,France,0,0,1,0,0,0,0,2
+Villa Marquis Member of Meliá Collection,"8th arr., Paris",8.6,8.9,Deluxe Room,1 queen bed,1480.054,Paris,France,0,0,1,0,0,0,0,1
+Hôtel Elysia,"8th arr., Paris",9.0,9.4,Prestige Suite,"2 beds (1 king, 1 sofa bed)",3642.429,Paris,France,0,0,0,1,0,0,0,2
+Hôtel Chateaubriand,"8th arr., Paris",7.8,8.4,Classic Double Room,1 full bed,987.047,Paris,France,0,1,0,0,0,0,0,1
+Magnifique studio deux pas de Trocadéro Tour Eiffel 4-P,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 twin, 1 sofa bed)",1105.95,Paris,France,1,0,0,0,0,0,0,2
+RODIER COZY FLAT,"9th arr., Paris",7.3,0.0,Studio Apartment,"2 beds (1 full, 1 sofa bed)",367.838,Paris,France,0,1,0,0,0,0,0,2
+Cosy courcelles,"17th arr., Paris",7.0,0.0,One-Bedroom Apartment,1 full bed,442.10200000000003,Paris,France,0,1,0,0,0,0,0,1
+"O'Lord, 4 Etoiles, Residence de Luxe Champs-Elysees","8th arr., Paris",9.0,9.2,One-Bedroom Apartment Comte de Clare,1 queen bed,1225.673,Paris,France,0,0,1,0,0,0,0,1
+Le M-Otel - Spacieux & Charmant - Montmartre,"18th arr., Paris",0.0,0.0,Villa,6 full beds,2274.154,Paris,France,0,6,0,0,0,0,0,6
+Hôtel Bleu de Grenelle,"15th arr., Paris",8.9,9.3,Large Superior Double Room,   1 double or 2 twins,822.6220000000001,Paris,France,2,0,0,0,0,0,0,2
+Suites & Hôtel Helzear Montparnasse,"14th arr., Paris",8.3,8.5,One-Bedroom Suite,"2 beds (1 full, 1 sofa bed)",950.2280000000001,Paris,France,0,1,0,0,0,0,0,2
+Cocosmann,"12th arr., Paris",0.0,0.0,One-Bedroom Apartment,,1040.505,Paris,France,0,0,0,0,0,0,0,0
+Apartments WS Champs-Elysées - Colomb,"8th arr., Paris",8.3,8.7,Standard Studio,1 full bed,726.16,Paris,France,0,1,0,0,0,0,0,1
+Normandy Le Chantier,"1st arr., Paris",8.2,8.8,Superior King Room - Afterworks,1 king bed,892.674,Paris,France,0,0,0,1,0,0,0,1
+GuestReady - Calming escape in the 11th arr,"11th arr., Paris",8.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",726.568,Paris,France,0,1,0,0,0,0,0,2
+"Modern, Stylish Bright Apartment next to the Famous Paris Catacombs - 14th Arrondisement","14th arr., Paris",6.7,0.0,Apartment,1 full bed,449.76,Paris,France,0,1,0,0,0,0,0,1
+Mama Shelter Paris West,"15th arr., Paris",8.5,9.1,XL Mama Room,1 queen bed,551.306,Paris,France,0,0,1,0,0,0,0,1
+Hôtel Aladin,"13th arr., Paris",6.3,0.0,Standard Twin Room,2 twin beds,271.434,Paris,France,2,0,0,0,0,0,0,2
+Cute and Bright Studio in a Courtyard-Montmartre(5),"18th arr., Paris",8.9,8.8,Superior Studio,1 full bed,642.614,Paris,France,0,1,0,0,0,0,0,1
+Hôtel Opéra Dieppe,"9th arr., Paris",6.6,0.0,Twin Room,2 twin beds,424.812,Paris,France,2,0,0,0,0,0,0,2
+Elegant Appartement near the Arc,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,2 full beds,782.74,Paris,France,0,2,0,0,0,0,0,2
+Hôtel du Romancier,"8th arr., Paris",8.0,8.5,Double Room,1 king bed,978.191,Paris,France,0,0,0,1,0,0,0,1
+Hotel Bowmann,"8th arr., Paris",8.8,9.5,Haussmann Junior Suite,1 king bed,2052.873,Paris,France,0,0,0,1,0,0,0,1
+Grand Appart Spacieux Rénové Paris Métro Wifi,"11th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",548.2760000000001,Paris,France,0,1,0,0,0,0,0,2
+Pied A Terre - Tuileries,"1st arr., Paris",9.4,10.0,Two-Bedroom Apartment,"4 beds (2 sofa beds, 2 queens)",2558.619,Paris,France,0,0,2,0,2,0,0,4
+Quartier Libre - Saint Georges,"9th arr., Paris",8.9,9.2,Superior Apartment,"2 beds (1 sofa bed, 1 queen)",1266.067,Paris,France,0,0,1,0,0,0,0,2
+Aparts CADET,"9th arr., Paris",7.4,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",916.693,Paris,France,0,1,0,0,0,0,0,2
+Paris j'Adore Hotel & Spa,"17th arr., Paris",9.1,9.5,Deluxe - Precious with Jacuzzi,1 queen bed,1181.8120000000001,Paris,France,0,0,1,0,0,0,0,1
+HOTEL AU COEUR DE REPUBLIQUE,"11th arr., Paris",8.1,8.7,Superior Double Room with Street View,1 full bed,795.783,Paris,France,0,1,0,0,0,0,0,1
+Hôtel La Sanguine,"8th arr., Paris",7.7,0.0,Superior Double Room,1 full bed,694.89,Paris,France,0,1,0,0,0,0,0,1
+Quinzerie hôtel,"15th arr., Paris",9.2,9.6,Junior Suite with Garden View,"2 beds (1 king, 1 sofa bed)",2029.258,Paris,France,0,0,0,1,0,0,0,2
+"Hôtel de Berri Champs-Élysées, a Luxury Collection Hotel","8th arr., Paris",8.5,8.9,"Collection Suite, 1 King or 1 Twin/Single Beds",1 king bed,2712.948,Paris,France,0,0,0,1,0,0,0,1
+"The Hoxton, Paris","2nd arr., Paris",8.7,9.1,Cozy,1 queen bed,1013.735,Paris,France,0,0,1,0,0,0,0,1
+Loft Marais Sébastopol CityCosy,"3rd arr., Paris",6.7,0.0,Loft,"4 beds (3 fulls, 1 sofa bed)",1036.117,Paris,France,0,3,0,0,0,0,0,4
+Luxe Apartment 165m2 8pers Victor Hugo trocadero foch Champs Elysées,"16th arr., Paris",8.6,8.6,Deluxe Apartment,"7 beds (4 twins, 1 king, 2 sofa beds)",4386.204,Paris,France,4,0,0,1,2,0,0,7
+Le Narcisse Blanc,"7th arr., Paris",9.2,9.7,Superior Double Room,1 full bed,1982.4950000000001,Paris,France,0,1,0,0,0,0,0,1
+Hotel Eden,"15th arr., Paris",8.5,8.9,Superior double room with Spa access and massages (2 adults),1 full bed,804.056,Paris,France,0,1,0,0,0,0,0,1
+La Maison Favart,"2nd arr., Paris",9.0,9.4,Family Suite (4 People),1 queen bed,2401.129,Paris,France,0,0,1,0,0,0,0,1
+26 Faubourg - Ex-Hotel de Reims,"12th arr., Paris",7.9,8.5,Double Room,1 full bed,432.499,Paris,France,0,1,0,0,0,0,0,1
+Maison Colbert Member of Meliá Collection,"5th arr., Paris",8.7,9.2,Singular Room,Multiple bed types,1689.385,Paris,France,0,0,0,0,0,0,0,0
+Meliá Paris Vendôme,"1st arr., Paris",8.0,8.5,Premium Double Room,1 king bed,1146.911,Paris,France,0,0,0,1,0,0,0,1
+Villa Lutèce Port Royal,"13th arr., Paris",7.1,0.0,Junior Suite,1 queen bed,898.249,Paris,France,0,0,1,0,0,0,0,1
+Hôtel Montana La Fayette - Paris Gare du Nord,"10th arr., Paris",3.4,0.0,Double Room,1 full bed,355.074,Paris,France,0,1,0,0,0,0,0,1
+Bright and Homely 1 Bedroom rooftop Apartment in the Marais in Paris,"4th arr., Paris",0.0,0.0,Apartment,1 full bed,957.9590000000001,Paris,France,0,1,0,0,0,0,0,1
+Grand Studio Confort#St Germain-Montparnasse#4P,"6th arr., Paris",0.0,0.0,Studio,"2 beds (1 sofa bed, 1 queen)",499.61,Paris,France,0,0,1,0,0,0,0,2
+Pick A Flat's Apartment- Rue de Navarin,"9th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",486.63800000000003,Paris,France,0,0,1,0,0,0,0,2
+"Cœur 16eme, standing et calme","16th arr., Paris",8.6,9.4,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",725.3480000000001,Paris,France,0,0,1,0,0,0,0,2
+Brand new Parisian nest near the Arc de Triomphe,"8th arr., Paris",7.0,0.0,Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",701.793,Paris,France,2,1,0,0,0,0,0,4
+Residence Chatillon,"14th arr., Paris",5.4,0.0,Double Room,1 full bed,391.509,Paris,France,0,1,0,0,0,0,0,1
+BONJOUR,"15th arr., Paris",3.2,0.0,One-Bedroom Apartment,1 full bed,274.211,Paris,France,0,1,0,0,0,0,0,1
+Amazing apartment-2P-Sacré Coeur-Poissonniers-4,"18th arr., Paris",6.0,0.0,Studio,1 full bed,296.985,Paris,France,0,1,0,0,0,0,0,1
+CMG Championnet - Saint Ouen - Guy Môquet,"18th arr., Paris",8.0,8.6,Studio,"2 beds (1 full, 1 sofa bed)",407.36,Paris,France,0,1,0,0,0,0,0,2
+Hôtel des Beaux Arts,"13th arr., Paris",6.7,0.0,Double Room without Window,1 full bed,369.277,Paris,France,0,1,0,0,0,0,0,1
+Cosy apartment Montparnasse/Parc des Expositions (Jean Moulin),"14th arr., Paris",7.0,0.0,Apartment,"2 beds (1 full, 1 sofa bed)",415.394,Paris,France,0,1,0,0,0,0,0,2
+Paris superbe appartement Matignon,"8th arr., Paris",9.4,10.0,One-Bedroom Apartment,2 full beds,915.498,Paris,France,0,2,0,0,0,0,0,2
+Duplex de luxe,"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"4 beds (2 sofa beds, 2 queens)",414.253,Paris,France,0,0,2,0,2,0,0,4
+"Luxueux Flat, vue Tour Effiel","16th arr., Paris",10.0,10.0,Two-Bedroom Apartment,"4 beds (1 twin, 2 fulls, 1 queen)",1937.9360000000001,Paris,France,1,2,1,0,0,0,0,4
+Champs Elysées deux pas de l Etoile Tour Eiffel,"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",2274.56,Paris,France,0,2,0,0,0,0,0,3
+Le patio sur deux étages,"19th arr., Paris",5.8,0.0,One-Bedroom Apartment,1 full bed,460.551,Paris,France,0,1,0,0,0,0,0,1
+CMG Résidence Saint Lazare,"8th arr., Paris",6.1,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",576.1560000000001,Paris,France,0,1,0,0,0,0,0,2
+Parisian Cocoon 1bdr Flat - Prime Loc le Marais,"3rd arr., Paris",7.8,8.3,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",968.864,Paris,France,0,1,0,0,0,0,0,2
+16ième Très beau appartement Parc des princes / Roland garros,Paris,0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",736.346,Paris,France,0,1,0,0,0,0,0,2
+Modern's Hotel,"12th arr., Paris",3.2,0.0,Standard Double Room,1 full bed,302.753,Paris,France,0,1,0,0,0,0,0,1
+Appartement 2 pièces Palais des Congrès proche Arc de Triomphe,"17th arr., Paris",0.0,0.0,One-Bedroom Apartment,"3 beds (1 full, 2 sofa beds)",771.972,Paris,France,0,1,0,0,2,0,0,3
+St Michel - Deluxe Apartment - Harpe,"5th arr., Paris",6.7,0.0,Apartment with Terrace,"3 beds (1 twin, 1 full, 1 sofa bed)",1225.695,Paris,France,1,1,0,0,0,0,0,3
+Appartement paris Trocadero - Passy,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,255.166,Paris,France,0,1,0,0,0,0,0,1
+indépendant flat: canal st martin&trains stations!,"19th arr., Paris",7.8,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",497.208,Paris,France,0,1,0,0,0,0,0,2
+Blue Nights Sébastopol 110,"3rd arr., Paris",6.7,0.0,Superior Studio,"2 beds (1 king, 1 sofa bed)",419.938,Paris,France,0,0,0,1,0,0,0,2
+DUPLEX - 2 Bedrooms - PERE LACHAISE - Paris 20e !,"20th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"2 beds (1 full, 1 queen)",494.666,Paris,France,0,1,1,0,0,0,0,2
+Flat magnifique 4 P arc triomphe,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",1259.989,Paris,France,0,1,0,0,0,0,0,2
+Cosy Bedroom - Tour Eiffel Champs de Mars 17B,"15th arr., Paris",1.0,0.0,Double Room,1 full bed,312.504,Paris,France,0,1,0,0,0,0,0,1
+8.Studio 2Pers#Sèvres-Lecourbe#Necker,"15th arr., Paris",8.7,10.0,Studio,1 queen bed,423.582,Paris,France,0,0,1,0,0,0,0,1
+La Résidence du Sénateur,"6th arr., Paris",8.8,9.3,Apartment with Garden View,"5 beds (2 twins, 1 king, 1 sofa bed, 1 queen)",4764.775000000001,Paris,France,2,0,1,1,0,0,0,5
+Joli Appart 4pers#Buttes Chaumont#Chapeau Rouge,"19th arr., Paris",5.9,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",442.067,Paris,France,0,1,0,0,0,0,0,2
+Exceptional duplex apartment bathed in sunshine,"8th arr., Paris",0.0,0.0,Duplex Apartment,"9 beds (4 kings, 3 sofa beds, 2 queens)",2230.141,Paris,France,0,0,2,4,3,0,0,9
+Two-Bedroom Apartment Champs-Elysées,"8th arr., Paris",9.5,10.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 king, 1 queen)",1474.252,Paris,France,2,0,1,1,0,0,0,4
+19 Atelier Montorgueil,"2nd arr., Paris",5.0,0.0,Apartment - Ground Floor,"4 beds (2 fulls, 2 sofa beds)",702.166,Paris,France,0,2,0,0,2,0,0,4
+Magnifique flat avec balcon Champs-Elysées. AC,"8th arr., Paris",8.0,0.0,Two-Bedroom Apartment,"4 beds (2 fulls, 2 sofa beds)",2251.759,Paris,France,0,2,0,0,2,0,0,4
+CMG Résidence Montmartre - Cottages,"18th arr., Paris",6.9,0.0,Apartment,"2 beds (1 sofa bed, 1 queen)",457.865,Paris,France,0,0,1,0,0,0,0,2
+Duplex luxe,"11th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"3 beds (1 sofa bed, 2 queens)",331.402,Paris,France,0,0,2,0,0,0,0,3
+Veeve - Village Vision,"13th arr., Paris",0.0,0.0,Apartment,3 full beds,1290.373,Paris,France,0,3,0,0,0,0,0,3
+Luxe Marais Bretagne CityCosy,"3rd arr., Paris",8.0,0.0,Apartment,"2 beds (1 full, 1 sofa bed)",1040.505,Paris,France,0,1,0,0,0,0,0,2
+CMG Faubourg Saint-Martin - Chaudron,"10th arr., Paris",4.5,0.0,Studio,"2 beds (1 full, 1 sofa bed)",311.398,Paris,France,0,1,0,0,0,0,0,2
+3.Appartement 6Pers#Sèvres-Lecourbe#Necker,"15th arr., Paris",10.0,10.0,One-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",861.994,Paris,France,0,2,0,0,0,0,0,3
+Nice Private Room in Center Paris,"13th arr., Paris",0.0,0.0,Double Room with Shared Bathroom,1 queen bed,631.126,Paris,France,0,0,1,0,0,0,0,1
+Nid douillet et chaleureux pour se détendre,"16th arr., Paris",5.3,0.0,Studio,"2 beds (1 twin, 1 full)",292.414,Paris,France,1,1,0,0,0,0,0,2
+Artirama,"1st arr., Paris",0.0,0.0,Two-Bedroom Apartment,,1664.808,Paris,France,0,0,0,0,0,0,0,0
+Eiffel Tower appartement,"7th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1662.413,Paris,France,0,2,0,0,0,0,0,3
+Maison rudigier,"7th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1291.493,Paris,France,0,1,0,0,0,0,0,1
+Maison perchelot,"9th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1072.4270000000001,Paris,France,0,1,0,0,0,0,0,1
+Michka’s home,"9th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,766.367,Paris,France,0,1,0,0,0,0,0,1
+Charming apartment for 2 people - Paris 18,"18th arr., Paris",0.0,0.0,Apartment,1 full bed,268.061,Paris,France,0,1,0,0,0,0,0,1
+Nice apartment for 4 people - Paris 10,"10th arr., Paris",0.0,0.0,Apartment,"2 beds (1 full, 1 sofa bed)",412.01,Paris,France,0,1,0,0,0,0,0,2
+Luxurious 2 Bedroom with Huge Private Terrace,"17th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",748.929,Paris,France,0,2,0,0,0,0,0,3
+Superb penthouse apartment,"8th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"5 beds (3 kings, 2 sofa beds)",1139.029,Paris,France,0,0,0,3,2,0,0,5
+Studio proche Invalides et Unesco,"7th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",554.855,Paris,France,0,1,0,0,0,0,0,2
+Emily 's in Paris,"5th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"2 beds (1 full, 1 queen)",1668.39,Paris,France,0,1,1,0,0,0,0,2
+Nice apartment for 4 people - Paris 17,"17th arr., Paris",0.0,0.0,Apartment,"2 beds (1 full, 1 sofa bed)",458.623,Paris,France,0,1,0,0,0,0,0,2
+Bright studio for 2 people - Paris 8,"8th arr., Paris",0.0,0.0,Apartment,1 full bed,340.60200000000003,Paris,France,0,1,0,0,0,0,0,1
+Luxurious 2 Bedroom with Huge Private Terrace,"17th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",658.5550000000001,Paris,France,0,2,0,0,0,0,0,3
+Quai de la Loire Test,"19th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (1 full, 1 sofa bed, 1 queen)",15351.714,Paris,France,0,1,1,0,0,0,0,3
+Appartement quartier Montorgueil !,"2nd arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",1713.056,Paris,France,0,1,0,0,0,0,0,2
+GuestReady - A touch of emerald with Eiffel view,"16th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"3 beds (1 full, 1 king, 1 queen)",2253.4990000000003,Paris,France,0,1,1,1,0,0,0,3
+Stunning architect studio near the Marais,"11th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 queen bed,351.94100000000003,Paris,France,0,0,1,0,0,0,0,1
+Sublim apartment Champs Elysée (ponthieu),"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1181.797,Paris,France,0,2,0,0,0,0,0,3
+Studio Avenue des Champs-Elysées,"8th arr., Paris",1.0,0.0,Studio,2 full beds,414.253,Paris,France,0,2,0,0,0,0,0,2
+MyKeypers - Deluxe Cosy Flat 4P - République,"10th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",676.381,Paris,France,0,1,0,0,0,0,0,2
+HSH Luxueux Appartement Belles Feuilles Trocadero,"16th arr., Paris",0.0,0.0,Two-Bedroom Apartment,2 full beds,3289.6530000000002,Paris,France,0,2,0,0,0,0,0,2
+[New] Eiffel tower view studio / Paris Étoile,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,646.965,Paris,France,0,1,0,0,0,0,0,1
+New Apartment near Champs Elysées,"17th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 queen bed,609.195,Paris,France,0,0,1,0,0,0,0,1
+Lovely & cosy apartment Paris 15th,"15th arr., Paris",5.0,0.0,Two-Bedroom Apartment,"3 beds (1 sofa bed, 2 queens)",875.0600000000001,Paris,France,0,0,2,0,0,0,0,3
+Superbe Appartement Paris 17,"17th arr., Paris",8.0,10.0,Two-Bedroom Apartment,"3 beds (1 full, 1 sofa bed, 1 queen)",869.9300000000001,Paris,France,0,1,1,0,0,0,0,3
+EIFFEL TOWER,"7th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"4 beds (3 kings, 1 sofa bed)",2095.631,Paris,France,0,0,0,3,0,0,0,4
+Appartement - Arc de Triomphe,"17th arr., Paris",2.5,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",680.558,Paris,France,0,1,0,0,0,0,0,2
+Luxury Paris Trocadero I by Airsuite,"16th arr., Paris",0.0,0.0,Apartment,"2 beds (1 sofa bed, 1 queen)",1051.645,Paris,France,0,0,1,0,0,0,0,2
+CHAMPS-ELySÉES,"16th arr., Paris",5.0,0.0,Two-Bedroom Apartment,"3 beds (1 sofa bed, 2 queens)",1308.8990000000001,Paris,France,0,0,2,0,0,0,0,3
+LUXURY ARC DE TRiOMPHE,"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 king, 1 sofa bed)",1315.861,Paris,France,2,0,0,1,0,0,0,4
+SUITE CHAMPS-ELySÉES,"8th arr., Paris",0.0,0.0,Deluxe Suite with Spa Bath,1 king bed,1294.9750000000001,Paris,France,0,0,0,1,0,0,0,1
+Appartement near to Montparnasse,"14th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 king bed,377.701,Paris,France,0,0,0,1,0,0,0,1
+Agréable Appartement parking - quai de la seine,"19th arr., Paris",8.0,0.0,Two-Bedroom Apartment,2 full beds,853.163,Paris,France,0,2,0,0,0,0,0,2
+Jean-Marie,"12th arr., Paris",0.0,0.0,One-Bedroom Villa,"6 beds (1 twin, 1 full, 1 king, 2 sofa beds, 1 queen)",1346.3210000000001,Paris,France,1,1,1,1,2,0,0,6
+ARC DE TRiUMPH 4 BEDROOMS,"8th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"4 beds (3 kings, 1 sofa bed)",2924.136,Paris,France,0,0,0,3,0,0,0,4
+GuestReady - Charming classic-style apartment,"18th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,24365.364,Paris,France,0,1,0,0,0,0,0,1
+Modern & Design Loft-Style Apartment in Paris 16th,"16th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1864.137,Paris,France,0,1,0,0,0,0,0,1
+Paris apartment,"16th arr., Paris",0.0,0.0,Standard Apartment,"2 beds (1 full, 1 sofa bed)",925.976,Paris,France,0,1,0,0,0,0,0,2
+43 Mezzanine 5Pers Madeleine Gallerie La Fayette,"8th arr., Paris",9.8,10.0,One-Bedroom Apartment,"3 beds (1 full, 2 sofa beds)",836.616,Paris,France,0,1,0,0,2,0,0,3
+AMAZING FLAT IN GREAT LOCALISATION in 6 AREAPARIS,"7th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 king bed,2029.838,Paris,France,0,0,0,1,0,0,0,1
+CMG Cosy Studio 2P - Parc Buttes Chaumont,"19th arr., Paris",5.0,0.0,Studio,1 full bed,469.988,Paris,France,0,1,0,0,0,0,0,1
+Appartement calme.,"18th arr., Paris",0.0,0.0,Two-Bedroom Apartment,3 full beds,609.195,Paris,France,0,3,0,0,0,0,0,3
+Palmers face Hyatt Regency,"17th arr., Paris",10.0,10.0,Apartment,"5 beds (3 sofa beds, 2 queens)",1812.616,Paris,France,0,0,2,0,3,0,0,5
+Countryside in Montmartre,"18th arr., Paris",0.0,0.0,One-Bedroom Villa,1 queen bed,1340.229,Paris,France,0,0,1,0,0,0,0,1
+Studio type loft,"19th arr., Paris",0.0,0.0,Studio Apartment,"2 beds (1 full, 1 sofa bed)",285.834,Paris,France,0,1,0,0,0,0,0,2
+L'appartement Matignon,"8th arr., Paris",0.0,0.0,Three-Bedroom Apartment,"3 beds (1 full, 2 queens)",3655.17,Paris,France,0,1,2,0,0,0,0,3
+Appartement terrasse Paris,"16th arr., Paris",5.5,0.0,Deluxe Apartment,"4 beds (1 full, 3 queens)",1462.068,Paris,France,0,1,3,0,0,0,0,4
+Nice and calm flat with terrace close to Buttes-Chaumont in Paris - Welkeys,"19th arr., Paris",8.0,0.0,Apartment,2 full beds,589.222,Paris,France,0,2,0,0,0,0,0,2
+Loft au cœur du 17 eme,"17th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,438.62,Paris,France,0,1,0,0,0,0,0,1
+"Center Paris gorgeous apartment, near Eiffel Tower","15th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"4 beds (2 twins, 1 king, 1 sofa bed)",932.068,Paris,France,2,0,0,1,0,0,0,4
+Appartement Paris 11e,"11th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",414.253,Paris,France,0,1,0,0,0,0,0,2
+Studio meublé Paris 14eme,"14th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1553.4470000000001,Paris,France,0,1,0,0,0,0,0,1
+Cocooning apartment in the Montmartre area,"18th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,414.253,Paris,France,0,1,0,0,0,0,0,1
+Amazing Studio 2P- Place de clichy,"17th arr., Paris",0.0,0.0,Studio,1 full bed,583.944,Paris,France,0,1,0,0,0,0,0,1
+Amazing apartment III Saint Fargeau,"20th arr., Paris",3.5,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",378.995,Paris,France,0,1,0,0,0,0,0,2
+Charming appart 6P - Etienne Marcel / Marais,"3rd arr., Paris",5.5,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1276.556,Paris,France,0,2,0,0,0,0,0,3
+Appartement champs Élysées,"8th arr., Paris",0.0,0.0,One-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1159.907,Paris,France,0,2,0,0,0,0,0,3
+Cosy Studio,"15th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",745.655,Paris,France,0,1,0,0,0,0,0,2
+GuestReady - Chic Apt 7 mins to Pantheon,"5th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,24365.364,Paris,France,0,1,0,0,0,0,0,1
+GuestReady - Cozy Apt 10 mins from Arc de Triomphe,"16th arr., Paris",6.5,0.0,One-Bedroom Apartment,1 full bed,620.41,Paris,France,0,1,0,0,0,0,0,1
+GuestReady - Vibrant & Cosy in Montparnasse,"14th arr., Paris",7.0,0.0,Two-Bedroom Apartment,2 full beds,703.751,Paris,France,0,2,0,0,0,0,0,2
+Beautiful apartment with a view of Eiffel Tower in center Paris,"14th arr., Paris",7.1,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",670.231,Paris,France,0,1,0,0,0,0,0,2
+appartement trocadero paris 16,"16th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"2 beds (1 full, 1 bunk bed)",1657.01,Paris,France,0,1,0,0,0,0,0,2
+Lovely Center Residence,"1st arr., Paris",7.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",704.821,Paris,France,0,1,0,0,0,0,0,2
+Magnificent Luxury Flat 2BR/6P - Champs-Elysées,"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (1 sofa bed, 2 queens)",2492.362,Paris,France,0,0,2,0,0,0,0,3
+HSH Luxury Apartment Montaigne Francois 1er,"8th arr., Paris",8.6,8.4,Two-Bedroom Apartment,2 full beds,1442.756,Paris,France,0,2,0,0,0,0,0,2
+Appartement au coeur du Marais à Paris by Weekome,"4th arr., Paris",7.8,8.2,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",634.143,Paris,France,0,2,0,0,0,0,0,3
+Appartement centre de Paris 2 terrasses au pied des Buttes de Chaumont,"19th arr., Paris",10.0,10.0,Two-Bedroom Apartment,"4 beds (3 sofa beds, 1 queen)",1983.191,Paris,France,0,0,1,0,3,0,0,4
+Haussmannien moderne 2 pièces quartier Europe du 8éme,"8th arr., Paris",0.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",670.115,Paris,France,0,1,0,0,0,0,0,2
+CMG - Sacré Coeur / Montmartre,"18th arr., Paris",6.0,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",475.743,Paris,France,0,1,0,0,0,0,0,2
+Veeve - Boughs on the Breeze,"10th arr., Paris",0.0,0.0,Apartment,"3 beds (2 fulls, 1 queen)",1789.157,Paris,France,0,2,1,0,0,0,0,3
+Paris centre ville,"3rd arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1657.01,Paris,France,0,1,0,0,0,0,0,1
+CMG - Batignolles 1G,"17th arr., Paris",5.5,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",615.813,Paris,France,0,1,0,0,0,0,0,2
+Location chambre à 35 mn du Stade deFrance pour la Finale de la Ligue des Champions UEFA,"14th arr., Paris",0.0,0.0,One-Bedroom Apartment,1 full bed,1014.919,Paris,France,0,1,0,0,0,0,0,1
+Cosy studio near Buttes Chaumont and Philharmonie,"19th arr., Paris",9.0,10.0,Two-Bedroom Apartment,"4 beds (2 fulls, 2 sofa beds)",2278.389,Paris,France,0,2,0,0,2,0,0,4
+CMG Jacuzzi - Martyrs/ Bas Montmartre,"9th arr., Paris",5.2,0.0,Studio,1 full bed,536.689,Paris,France,0,1,0,0,0,0,0,1
+CMG - République / Sebastopol,"3rd arr., Paris",5.3,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",530.777,Paris,France,0,1,0,0,0,0,0,2
+CMG-Le Marais-Temple-1A,"3rd arr., Paris",6.2,0.0,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",630.412,Paris,France,0,0,1,0,0,0,0,2
+Sweet Inn - Maison Boissière,"16th arr., Paris",8.5,9.1,One-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",1184.762,Paris,France,2,0,0,0,0,0,0,3
+CMG - LE MARAIS/SAINT ANTOINE,"4th arr., Paris",7.5,8.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",795.213,Paris,France,0,1,0,0,0,0,0,2
+CMG Lamartine-Maubeuge,"9th arr., Paris",7.4,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",989.077,Paris,France,0,2,0,0,0,0,0,3
+High Stay Saint Germain de Pres Paris 6,"6th arr., Paris",9.5,9.6,Two-Bedroom Apartment,"3 beds (2 twins, 1 sofa bed)",1364.075,Paris,France,2,0,0,0,0,0,0,3
+CMG - Gare Saint Lazare,"9th arr., Paris",6.2,0.0,Two-Bedroom Apartment,"3 beds (1 sofa bed, 2 queens)",990.83,Paris,France,0,0,2,0,0,0,0,3
+CMG Ledru-Rollin - Canal Saint Martin 1,"11th arr., Paris",6.2,0.0,Studio,1 sofa bed,320.982,Paris,France,0,0,0,0,0,0,0,0
+CMG - Notre Dame de Paris,"5th arr., Paris",7.7,8.4,Two-Bedroom Apartment,"6 beds (3 twins, 1 full, 2 sofa beds)",1111.693,Paris,France,3,1,0,0,2,0,0,6
+CMG - Montorgeuil / Centre de Paris,"2nd arr., Paris",7.0,0.0,Studio,1 queen bed,486.901,Paris,France,0,0,1,0,0,0,0,1
+CMG Gambey / Paris 11ème,"11th arr., Paris",5.6,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",469.1,Paris,France,0,1,0,0,0,0,0,2
+CMG Delta / Montmartre,"9th arr., Paris",6.3,0.0,Two-Bedroom Apartment,2 full beds,842.167,Paris,France,0,2,0,0,0,0,0,2
+GuestReady - Modern 1-Bedroom Apartment Champs-Élysées,"8th arr., Paris",6.3,8.3,Apartment,"2 beds (1 sofa bed, 1 queen)",907.016,Paris,France,0,0,1,0,0,0,0,2
+CMG Ponthieu /Champs-Élysées,"8th arr., Paris",6.3,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1203.323,Paris,France,0,2,0,0,0,0,0,3
+CMG - Suite Deluxe Tour Eiffel 54,"16th arr., Paris",6.9,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",689.399,Paris,France,0,1,0,0,0,0,0,2
+CMG rue de Bretagne 2,"3rd arr., Paris",5.9,0.0,Studio,1 sofa bed,367.903,Paris,France,0,0,0,0,0,0,0,0
+CMG Jacques /Pantheon,"5th arr., Paris",7.0,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1255.27,Paris,France,0,2,0,0,0,0,0,3
+HSH Magnifique Appartement Marais Arts&Métiers,"3rd arr., Paris",6.5,0.0,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",481.421,Paris,France,0,1,0,0,0,0,0,2
+CMG Résidence République - Rue Béranger,"3rd arr., Paris",8.1,8.6,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1263.159,Paris,France,0,2,0,0,0,0,0,3
+"Champs Elysées!Montaigne, New Luxurious 6pers flat","8th arr., Paris",6.8,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1610.6770000000001,Paris,France,0,2,0,0,0,0,0,3
+CMG - Le Bon Marché - Sèvres D,"6th arr., Paris",6.4,8.0,Three-Bedroom Apartment,"4 beds (1 full, 1 sofa bed, 2 queens)",1740.074,Paris,France,0,1,2,0,0,0,0,4
+Greeter-Bel appartement Parisien de 70m2 - St Lazare,"8th arr., Paris",9.7,10.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1120.6870000000001,Paris,France,0,2,0,0,0,0,0,3
+Pavleo,"4th arr., Paris",8.0,10.0,Three-Bedroom Apartment,,1932.367,Paris,France,0,0,0,0,0,0,0,0
+Paris-sur-seine-chez-caroline,"4th arr., Paris",9.5,9.5,Deluxe Apartment,"6 beds (2 twins, 1 king, 1 sofa bed, 2 futons)",2504.6620000000003,Paris,France,2,0,0,1,0,0,0,6
+CMG Cherche Midi - Sèvres,"6th arr., Paris",6.9,0.0,Two-Bedroom Apartment,"3 beds (2 fulls, 1 sofa bed)",1156.306,Paris,France,0,2,0,0,0,0,0,3
+217361 - Appartement 6 personnes à Paris,"17th arr., Paris",8.8,8.3,Two-Bedroom Apartment,"4 beds (2 twins, 1 full, 1 sofa bed)",1235.4470000000001,Paris,France,2,1,0,0,0,0,0,4
+Club246 Paris Saint Martin,"3rd arr., Paris",4.3,0.0,   Quadruple Room,2 full beds,517.816,Paris,France,0,2,0,0,0,0,0,2
+Mb property,"15th arr., Paris",8.0,8.1,One-Bedroom Apartment,"2 beds (1 full, 1 sofa bed)",572.063,Paris,France,0,1,0,0,0,0,0,2
+Appartement de 2 chambres avec vue sur la ville balcon amenage et wifi a Paris,"8th arr., Paris",0.0,0.0,Two-Bedroom Apartment,"3 beds (1 twin, 2 fulls)",9107.566,Paris,France,1,2,0,0,0,0,0,3
+CMG Residence Buttes Chaumont - Botzaris,"19th arr., Paris",5.4,0.0,Studio,1 sofa bed,293.176,Paris,France,0,0,0,0,0,0,0,0
+Stella Yardena,"17th arr., Paris",9.0,0.0,Apartment,"2 beds (1 sofa bed, 1 queen)",731.034,Paris,France,0,0,1,0,0,0,0,2
+Les Residences - République,"3rd arr., Paris",6.9,0.0,One-Bedroom Apartment,"2 beds (1 sofa bed, 1 queen)",888.38,Paris,France,0,0,1,0,0,0,0,2
+Love Nest in Saint-Michel,"6th arr., Paris",6.7,8.3,Deluxe Apartment,1 queen bed,974.712,Paris,France,0,0,1,0,0,0,0,1
+Narcisse,"3rd arr., Paris",3.7,0.0,Three-Bedroom Apartment,"3 beds (1 full, 1 king, 1 queen)",1802.5140000000001,Paris,France,0,1,1,1,0,0,0,3
+CMG - Suite Premium Tour Eiffel - 71,"16th arr., Paris",6.4,0.0,Superior Double Room,1 queen bed,597.95,Paris,France,0,0,1,0,0,0,0,1
+Balthazar,"3rd arr., Paris",6.3,0.0,Deluxe Apartment,,2166.475,Paris,France,0,0,0,0,0,0,0,0
+Sublime appartement Champs Elysees ( Chaillot),"16th arr., Paris",0.0,0.0,Apartment with Terrace,1 full bed,1448.289,Paris,France,0,1,0,0,0,0,0,1
+Haussmann Opera,"9th arr., Paris",6.1,0.0,Deluxe Apartment,"6 beds (2 twins, 2 fulls, 1 sofa bed, 1 queen)",1645.871,Paris,France,2,2,1,0,0,0,0,6
+Résidence Bonne Nouvelle,"2nd arr., Paris",5.8,0.0,Appartment-1,"2 beds (1 sofa bed, 1 queen)",872.367,Paris,France,0,0,1,0,0,0,0,2
+Boutique Hotel de la Place des Vosges,"4th arr., Paris",8.5,8.9,Double Room,1 full bed,1094.663,Paris,France,0,1,0,0,0,0,0,1
+Montparnasse Alésia,"14th arr., Paris",8.1,8.5,Standard Single Room,,1023.448,Paris,France,0,0,0,0,0,0,0,0
+Oops! Latin quarter by Hiphophostels,"13th arr., Paris",7.3,0.0,Bed in 2-Bed Mixed Dormitory Room,,319.798,Paris,France,0,0,0,0,0,0,0,0
+Hipotel Paris Père-Lachaise République,"11th arr., Paris",5.1,0.0,Single Room,,413.788,Paris,France,0,0,0,0,0,0,0,0
+Modern Hotel,"11th arr., Paris",6.5,0.0,Single Room,,574.326,Paris,France,0,0,0,0,0,0,0,0
+Villa Montparnasse,"14th arr., Paris",7.7,8.2,Single Room,,1090.452,Paris,France,0,0,0,0,0,0,0,0
+Strand Hotel,"Bencoolen, Singapore",7.7,8.1,Deluxe Double Room,1 queen bed,329.267,Singapore,Singapore,0,0,1,0,0,0,0,1
+Kranji Sanctuary Resort,Singapore,8.0,0.0,Standard Double Room,1 queen bed,323.267,Singapore,Singapore,0,0,1,0,0,0,0,1
+Republic of Singapore Yacht Club,"Queenstown, Singapore",6.5,0.0,Superior Double or Twin Room,Multiple bed types,320.083,Singapore,Singapore,0,0,0,0,0,0,0,0
+Grand Copthorne Waterfront,"Robertson Quay, Singapore",7.3,0.0,Grand Deluxe King ( Newly Renovated Room),1 full bed,457.83,Singapore,Singapore,0,1,0,0,0,0,0,1
+Studio M Hotel,"Robertson Quay, Singapore",6.9,0.0,Premier Loft,1 queen bed,364.592,Singapore,Singapore,0,0,1,0,0,0,0,1
+AM Hotel,"East Coast, Singapore",6.5,0.0,Standard Double or Twin Room,   1 double or 2 twins,178.377,Singapore,Singapore,2,0,0,0,0,0,0,2
+Rendezvous Hotel Singapore by Far East Hospitality,"Bencoolen, Singapore",8.5,9.0,Deluxe Double or Twin Room,   1 double or 2 twins,477.257,Singapore,Singapore,2,0,0,0,0,0,0,2
+M Hotel Singapore City Centre,"Shenton Way, Singapore",7.9,8.5,Deluxe Twin Room with Free Wifi,2 twin beds,427.175,Singapore,Singapore,2,0,0,0,0,0,0,2
+Arena eSports Hotel @ Bugis Village,"Bugis, Singapore",7.1,0.0,Queen Room with Shared Bathroom,1 queen bed,209.707,Singapore,Singapore,0,0,1,0,0,0,0,1
+"Holiday Inn Express Singapore Serangoon, an IHG Hotel","Lavender, Singapore",8.2,8.6,Standard Room,   1 double or 2 twins,348.169,Singapore,Singapore,2,0,0,0,0,0,0,2
+Hotel 81 Premier Princess,"Geylang, Singapore",8.1,8.3,Deluxe Queen Room,1 full bed,213.79,Singapore,Singapore,0,1,0,0,0,0,0,1
+M Social Singapore,"Robertson Quay, Singapore",7.5,8.1,Alcove Cozy Queen,1 queen bed,343.651,Singapore,Singapore,0,0,1,0,0,0,0,1
+"Holiday Inn Express Singapore Katong, an IHG Hotel","Katong, Singapore",8.4,9.0,Standard Room,   1 double or 2 twins,298.219,Singapore,Singapore,2,0,0,0,0,0,0,2
+Value Hotel Balestier,"Novena, Singapore",6.6,0.0,Standard Double Room,1 queen bed,173.873,Singapore,Singapore,0,0,1,0,0,0,0,1
+lyf one-north Singapore,"Queenstown, Singapore",8.4,8.7,Studio,1 queen bed,294.285,Singapore,Singapore,0,0,1,0,0,0,0,1
+Orchard Hotel Singapore,"Orchard, Singapore",7.6,8.2,Premier King Room,Multiple bed types,503.851,Singapore,Singapore,0,0,0,0,0,0,0,0
+"Holiday Inn Singapore Little India, an IHG Hotel","Farrer Park, Singapore",8.3,8.6,2 Twin Beds High Floor - Smoking,2 twin beds,376.259,Singapore,Singapore,2,0,0,0,0,0,0,2
+"Holiday Inn Singapore Orchard City Centre, an IHG Hotel","Orchard, Singapore",8.0,8.3,Premium King Room,1 king bed,717.399,Singapore,Singapore,0,0,0,1,0,0,0,1
+Ibis Singapore Novena,"Novena, Singapore",7.3,0.0,Standard Queen Room,1 queen bed,252.11700000000002,Singapore,Singapore,0,0,1,0,0,0,0,1
+Momentus Hotel Alexandra,"Bukit Merah, Singapore",7.5,9.2,Deluxe Queen Room,1 queen bed,415.94800000000004,Singapore,Singapore,0,0,1,0,0,0,0,1
+Mercure Singapore On Stevens,"Orchard, Singapore",7.5,8.2,Superior Double Room,1 full bed,385.214,Singapore,Singapore,0,1,0,0,0,0,0,1
+Oasia Resort Sentosa by Far East Hospitality,"Sentosa Island, Singapore",8.5,9.0,Junior Suite,1 king bed,836.275,Singapore,Singapore,0,0,0,1,0,0,0,1
+Robertson Quay Hotel,"Robertson Quay, Singapore",5.5,0.0,Standard Double Room,1 queen bed,250.731,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel Telegraph,"Marina Bay, Singapore",8.8,9.1,Cozy Room,1 king bed,711.985,Singapore,Singapore,0,0,0,1,0,0,0,1
+The Outpost Hotel Sentosa by Far East Hospitality,"Sentosa Island, Singapore",8.3,9.1,Deluxe Room Sea View,1 full bed,743.8340000000001,Singapore,Singapore,0,1,0,0,0,0,0,1
+The Capitol Kempinski Hotel Singapore,"City Hall, Singapore",8.8,9.4,Stamford Suite,1 king bed,1650.575,Singapore,Singapore,0,0,0,1,0,0,0,1
+Quincy Hotel Singapore by Far East Hospitality,"Orchard, Singapore",8.7,9.0,Deluxe Room,1 king bed,582.598,Singapore,Singapore,0,0,0,1,0,0,0,1
+Orchard Rendezvous Hotel by Far East Hospitality,"Orchard, Singapore",8.0,8.5,Club Double or Twin Room,   1 double or 2 twins,631.327,Singapore,Singapore,2,0,0,0,0,0,0,2
+Pan Pacific Singapore,"Marina Bay, Singapore",8.4,8.9,Deluxe Panoramic King or Twin Room,Multiple bed types,779.823,Singapore,Singapore,0,0,0,0,0,0,0,0
+Fraser Place Robertson Walk Singapore,"Robertson Quay, Singapore",8.8,9.3,One Bedroom Apartment Deluxe,1 queen bed,635.388,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel 81 Premier Star,"Geylang, Singapore",7.2,0.0,Twin Room,2 twin beds,170.154,Singapore,Singapore,2,0,0,0,0,0,0,2
+Amara Singapore,"Shenton Way, Singapore",6.9,0.0,Executive Room,   1 double or 2 twins,423.99,Singapore,Singapore,2,0,0,0,0,0,0,2
+voco Orchard Singapore,"Orchard, Singapore",8.0,8.4,Deluxe Room,1 king bed,581.244,Singapore,Singapore,0,0,0,1,0,0,0,1
+YOTEL Singapore Orchard Road,"Orchard, Singapore",8.0,8.3,Premium Queen Room,1 queen bed,364.374,Singapore,Singapore,0,0,1,0,0,0,0,1
+"Holiday Inn Express Singapore Clarke Quay, an IHG Hotel","Clarke Quay, Singapore",8.2,8.6,Standard Twin Room,2 twin beds,411.253,Singapore,Singapore,2,0,0,0,0,0,0,2
+Ibis Singapore on Bencoolen,"Bencoolen, Singapore",7.8,8.2,Standard Twin Room,2 twin beds,356.79200000000003,Singapore,Singapore,2,0,0,0,0,0,0,2
+"Holiday Inn Singapore Atrium, an IHG Hotel","Robertson Quay, Singapore",7.7,8.1,Standard Queen Room - Lounge Access,1 queen bed,538.248,Singapore,Singapore,0,0,1,0,0,0,0,1
+lyf Farrer Park Singapore,"Lavender, Singapore",8.5,8.8,One of a Kind,1 queen bed,433.943,Singapore,Singapore,0,0,1,0,0,0,0,1
+Heritage Collection on Pagoda - A Digital Hotel,"Chinatown, Singapore",0.0,0.0,Loft (Skylight),1 queen bed,302.043,Singapore,Singapore,0,0,1,0,0,0,0,1
+Value Hotel Nice,"Novena, Singapore",6.7,0.0,Standard Queen Room ( No Windows ),1 full bed,174.549,Singapore,Singapore,0,1,0,0,0,0,0,1
+Classique Hotel,"Lavender, Singapore",7.1,0.0,Standard Queen Room - no window,1 full bed,211.133,Singapore,Singapore,0,1,0,0,0,0,0,1
+The St Regis Singapore,"Orchard, Singapore",8.7,9.3,Grand Deluxe King Room,1 king bed,945.119,Singapore,Singapore,0,0,0,1,0,0,0,1
+K Hotel Aliwal (Premier),"Kampong Glam, Singapore",4.8,0.0,Standard Twin Room with Shared Bathroom,1 bunk bed,143.97,Singapore,Singapore,0,0,0,0,0,0,0,0
+Sheraton Towers Singapore Hotel,"Orchard, Singapore",8.1,8.8,Twin Room,2 twin beds,564.444,Singapore,Singapore,2,0,0,0,0,0,0,2
+Fraser Residence Orchard Singapore,"Orchard, Singapore",9.1,9.4,Studio Deluxe King,1 king bed,650.866,Singapore,Singapore,0,0,0,1,0,0,0,1
+Novotel Singapore On Stevens,"Orchard, Singapore",7.3,8.1,Superior Double Room,1 full bed,469.61400000000003,Singapore,Singapore,0,1,0,0,0,0,0,1
+"InterContinental Singapore Robertson Quay, an IHG Hotel","Robertson Quay, Singapore",8.3,9.0,1 King Bed Classic Top Floor,1 king bed,703.172,Singapore,Singapore,0,0,0,1,0,0,0,1
+Fragrance Hotel - Classic,"Novena, Singapore",7.1,0.0,Standard Double Room,1 full bed,158.454,Singapore,Singapore,0,1,0,0,0,0,0,1
+Genting Hotel Jurong,"Jurong East, Singapore",7.5,8.1,Deluxe Double or Twin Room,Multiple bed types,344.68600000000004,Singapore,Singapore,0,0,0,0,0,0,0,0
+Citadines Mount Sophia Singapore,"Selegie, Singapore",7.9,8.1,Studio,1 queen bed,412.166,Singapore,Singapore,0,0,1,0,0,0,0,1
+Days Hotel by Wyndham Singapore at Zhongshan Park,"Novena, Singapore",8.2,8.6,Superior Queen Room - Non-Smoking,1 queen bed,368.652,Singapore,Singapore,0,0,1,0,0,0,0,1
+Peninsula Excelsior Hotel,"City Hall, Singapore",7.6,0.0,Premier Double or Twin Room,Multiple bed types,520.996,Singapore,Singapore,0,0,0,0,0,0,0,0
+Singapore Marriott Tang Plaza Hotel,"Orchard, Singapore",8.1,8.8,"Premier Deluxe Twin, Guest room, 2 Single Beds",2 twin beds,776.32,Singapore,Singapore,2,0,0,0,0,0,0,2
+Ramada by Wyndham Singapore at Zhongshan Park,"Novena, Singapore",8.0,8.5,King Room with Park View - Non-Smoking,1 king bed,450.664,Singapore,Singapore,0,0,0,1,0,0,0,1
+Hmlet Cantonment,"Bukit Merah, Singapore",8.2,8.3,The Small Room,1 queen bed,407.231,Singapore,Singapore,0,0,1,0,0,0,0,1
+Village Hotel Changi by Far East Hospitality,"Changi, Singapore",8.0,8.5,Deluxe Double or Twin Room,Multiple bed types,333.523,Singapore,Singapore,0,0,0,0,0,0,0,0
+Vibe Hotel Singapore Orchard,"Orchard, Singapore",8.3,8.7,Premier Double or Twin Room,1 king bed,516.671,Singapore,Singapore,0,0,0,1,0,0,0,1
+Furama RiverFront,"Robertson Quay, Singapore",6.9,0.0,Superior Double or Twin Room,Multiple bed types,323.15500000000003,Singapore,Singapore,0,0,0,0,0,0,0,0
+Ibis Styles Singapore On Macpherson,Singapore,6.4,0.0,Standard Queen Room with Pool View,1 queen bed,249.887,Singapore,Singapore,0,0,1,0,0,0,0,1
+Mercure Singapore Tyrwhitt,"Lavender, Singapore",7.6,8.1,Classic Double Room with Balcony,1 full bed,383.657,Singapore,Singapore,0,1,0,0,0,0,0,1
+Hotel G Singapore,"Bencoolen, Singapore",7.9,8.4,Good Room King,1 king bed,308.509,Singapore,Singapore,0,0,0,1,0,0,0,1
+W Singapore - Sentosa Cove,"Sentosa Island, Singapore",8.5,9.1,Fabulous King,1 king bed,916.774,Singapore,Singapore,0,0,0,1,0,0,0,1
+Grand Mercure Singapore Roxy,"Katong, Singapore",7.9,8.2,Superior Double Room,1 king bed,380.19800000000004,Singapore,Singapore,0,0,0,1,0,0,0,1
+Citadines Rochor,"Little India, Singapore",8.4,8.8,Studio Executive,1 queen bed,448.27500000000003,Singapore,Singapore,0,0,1,0,0,0,0,1
+Harbour Ville Hotel,"Bukit Merah, Singapore",6.4,0.0,Premier Queen Room (Newly Renovated),1 full bed,262.754,Singapore,Singapore,0,1,0,0,0,0,0,1
+Owen House by Hmlet,"Farrer Park, Singapore",7.1,0.0,Studio,1 queen bed,394.491,Singapore,Singapore,0,0,1,0,0,0,0,1
+hovoh homes Bugis Rochor,"Victoria, Singapore",0.0,0.0,One-Bedroom Apartment,1 twin bed,514.664,Singapore,Singapore,1,0,0,0,0,0,0,1
+Four Seasons Hotel Singapore,"Orchard, Singapore",9.2,9.6,Boulevard Twin Room,2 twin beds,1220.215,Singapore,Singapore,2,0,0,0,0,0,0,2
+Fraser Suites Singapore,"River Valley, Singapore",8.5,8.9,Premier One-Bedroom Apartment,1 king bed,703.863,Singapore,Singapore,0,0,0,1,0,0,0,1
+JW Marriott Hotel Singapore South Beach,"City Hall, Singapore",8.4,8.9,Premier Guest Room with Marina Bay View,1 king bed,1003.164,Singapore,Singapore,0,0,0,1,0,0,0,1
+L Hotel at Broadway,"Farrer Park, Singapore",5.5,0.0,Deluxe Twin Room,2 twin beds,220.257,Singapore,Singapore,2,0,0,0,0,0,0,2
+K Hotel 1515,"Geylang, Singapore",5.2,0.0,Superior Double Room,1 queen bed,179.111,Singapore,Singapore,0,0,1,0,0,0,0,1
+Royal Plaza on Scotts,"Orchard, Singapore",7.6,8.3,Premier King Room,1 king bed,631.247,Singapore,Singapore,0,0,0,1,0,0,0,1
+Pullman Singapore Orchard,"Orchard, Singapore",8.2,8.5,Superior King Room,1 king bed,693.3530000000001,Singapore,Singapore,0,0,0,1,0,0,0,1
+Paradox Singapore Merchant Court at Clarke Quay,"Clarke Quay, Singapore",8.3,8.8,Luxe King,1 king bed,621.056,Singapore,Singapore,0,0,0,1,0,0,0,1
+Citadines Raffles Place Singapore,"Shenton Way, Singapore",8.7,9.0,Studio Deluxe,1 queen bed,601.946,Singapore,Singapore,0,0,1,0,0,0,0,1
+Dusit Thani Laguna Singapore,Singapore,8.4,9.1,Deluxe Laguna Pool View King,1 king bed,621.693,Singapore,Singapore,0,0,0,1,0,0,0,1
+YOTELAIR Singapore Changi Airport Landside,"Changi, Singapore",7.6,8.1,Premium Queen Cabin,1 queen bed,364.002,Singapore,Singapore,0,0,1,0,0,0,0,1
+"Maxwell Reserve Singapore, Autograph Collection","Chinatown, Singapore",7.4,0.0,"Le Colonial Room, Guest Room, 1 King",1 king bed,542.707,Singapore,Singapore,0,0,0,1,0,0,0,1
+"Duxton Reserve Singapore, Autograph Collection","Chinatown, Singapore",7.7,8.1,"Opium Club Suite, Club Lounge Access, 1 King, City View",1 king bed,1007.703,Singapore,Singapore,0,0,0,1,0,0,0,1
+Andaz Singapore A Concept by Hyatt,"Bugis, Singapore",8.6,9.2,1 King Sea View Deluxe,1 king bed,1210.5810000000001,Singapore,Singapore,0,0,0,1,0,0,0,1
+Courtyard by Marriott Singapore Novena,"Novena, Singapore",7.8,8.8,Guest Room Twin with Cityscape View,2 twin beds,589.71,Singapore,Singapore,2,0,0,0,0,0,0,2
+Mercure Singapore Bugis,"Victoria, Singapore",7.3,8.3,Signature Twin Room,2 twin beds,419.451,Singapore,Singapore,2,0,0,0,0,0,0,2
+"Hotel Indigo Singapore Katong, an IHG Hotel","Katong, Singapore",9.0,9.5,1 King Standard Heritage View,1 queen bed,541.107,Singapore,Singapore,0,0,1,0,0,0,0,1
+Villa Samadhi Singapore by Samadhi - Adults Only,"Bukit Merah, Singapore",8.1,8.7,Crib,1 king bed,586.237,Singapore,Singapore,0,0,0,1,0,0,0,1
+The Vagabond Club A Tribute Portfolio Hotel Singapore,"Jalan Besar, Singapore",8.1,9.3,"Courtyard Club Room, Club Lounge Access, Guest Room, 1 King",1 king bed,775.125,Singapore,Singapore,0,0,0,1,0,0,0,1
+Aqueen Hotel Paya Lebar,"Geylang, Singapore",7.9,8.3,Superior Room,   1 double or 2 twins,244.433,Singapore,Singapore,2,0,0,0,0,0,0,2
+The Westin Singapore,"Marina Bay, Singapore",8.5,9.1,Deluxe King Room,1 king bed,867.886,Singapore,Singapore,0,0,0,1,0,0,0,1
+Hotel Soloha at Chinatown,"Chinatown, Singapore",7.4,0.0,Standard Double No Window,1 queen bed,277.039,Singapore,Singapore,0,0,1,0,0,0,0,1
+Carlton City Hotel Singapore,"Chinatown, Singapore",8.5,9.0,Executive King or Twin Room,Multiple bed types,698.033,Singapore,Singapore,0,0,0,0,0,0,0,0
+Dorsett Singapore,"Chinatown, Singapore",7.4,0.0,Splash Room,1 king bed,407.225,Singapore,Singapore,0,0,0,1,0,0,0,1
+"PARKROYAL COLLECTION Pickering, Singapore","Chinatown, Singapore",8.7,9.3,Long Stay Offer - Urban Deluxe King or Twin,Multiple bed types,809.0450000000001,Singapore,Singapore,0,0,0,0,0,0,0,0
+"Capri by Fraser Changi City, Singapore","Changi, Singapore",8.2,8.8,Studio Deluxe,1 king bed,522.365,Singapore,Singapore,0,0,0,1,0,0,0,1
+Village Hotel Katong by Far East Hospitality,"Katong, Singapore",8.3,8.8,Peranakan Club,1 king bed,565.798,Singapore,Singapore,0,0,0,1,0,0,0,1
+Park Regis Singapore,"Clarke Quay, Singapore",7.7,8.2,Standard Double Room,1 queen bed,473.993,Singapore,Singapore,0,0,1,0,0,0,0,1
+The Fullerton Bay Hotel Singapore,"Marina Bay, Singapore",8.9,9.5,Bay View Room,1 king bed,1729.202,Singapore,Singapore,0,0,0,1,0,0,0,1
+The Fullerton Hotel Singapore,"Marina Bay, Singapore",8.8,9.4,Premier Collyer Suite,1 king bed,2088.917,Singapore,Singapore,0,0,0,1,0,0,0,1
+PARKROYAL Serviced Suites Singapore,"Bugis, Singapore",8.3,8.4,Premium One-Bedroom Apartment,1 full bed,755.697,Singapore,Singapore,0,1,0,0,0,0,0,1
+"The Ritz-Carlton, Millenia Singapore","Marina Bay, Singapore",9.0,9.4,Deluxe Kallang Guest room with 1 King Bed,1 king bed,1084.459,Singapore,Singapore,0,0,0,1,0,0,0,1
+"Crowne Plaza Changi Airport, an IHG Hotel","Changi, Singapore",8.6,9.2,1 King Bed Standard Jewel Wing,1 king bed,528.6,Singapore,Singapore,0,0,0,1,0,0,0,1
+"PARKROYAL on Kitchener Road, Singapore","Lavender, Singapore",7.7,8.3,Family Room (4 Adults),"3 beds (2 twins, 1 king)",1114.556,Singapore,Singapore,2,0,0,1,0,0,0,3
+"PARKROYAL on Beach Road, Singapore","Bugis, Singapore",8.4,9.0,PARKROYAL Club Premier Double or Twin Room,Multiple bed types,814.538,Singapore,Singapore,0,0,0,0,0,0,0,0
+"The Serangoon House, Singapore, a Tribute Portfolio Hotel","Farrer Park, Singapore",7.3,8.8,Royal Deluxe Room,2 queen beds,457.83,Singapore,Singapore,0,0,2,0,0,0,0,2
+"InterContinental Singapore, an IHG Hotel","Bugis, Singapore",8.4,9.0,Classic Room,   1 double or 2 twins,808.567,Singapore,Singapore,2,0,0,0,0,0,0,2
+The Scarlet Singapore,"Chinatown, Singapore",8.3,8.7,Suite,1 queen bed,909.6080000000001,Singapore,Singapore,0,0,1,0,0,0,0,1
+Grand Park City Hall,"City Hall, Singapore",7.8,8.5,Deluxe Double or Twin Room with Weekly Housekeeping,"3 beds (2 twins, 1 king)",523.677,Singapore,Singapore,2,0,0,1,0,0,0,3
+"PARKROYAL COLLECTION Marina Bay, Singapore","Marina Bay, Singapore",8.5,9.0,Signature Marina Bay King or Twin,Multiple bed types,979.357,Singapore,Singapore,0,0,0,0,0,0,0,0
+RedDoorz at Geylang,"Geylang, Singapore",6.6,0.0,Twin Room,2 twin beds,164.77700000000002,Singapore,Singapore,2,0,0,0,0,0,0,2
+JJH Serviced Apartments near Serangoon MRT,Singapore,7.0,0.0,One-Bedroom Apartment,1 queen bed,370.245,Singapore,Singapore,0,0,1,0,0,0,0,1
+Link Hotel Singapore,"Tiong Bahru, Singapore",6.8,0.0,Deluxe Double or Twin Room,   1 double or 2 twins,297.309,Singapore,Singapore,2,0,0,0,0,0,0,2
+New Cape Inn,"Tiong Bahru, Singapore",6.8,0.0,Standard King Room,1 king bed,269.201,Singapore,Singapore,0,0,0,1,0,0,0,1
+Ariva on Shan Serviced Residences,"Novena, Singapore",8.3,8.5,One-Bedroom Apartment,1 queen bed,386.488,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel Calmo Chinatown,"Chinatown, Singapore",5.6,0.0,Superior Double Room,1 queen bed,275.7,Singapore,Singapore,0,0,1,0,0,0,0,1
+Victoria Hotel a NuVe Group Collection,"Victoria, Singapore",6.8,0.0,Superior King Room,1 queen bed,290.344,Singapore,Singapore,0,0,1,0,0,0,0,1
+Lion Peak Hotel Hamilton,"Lavender, Singapore",6.7,0.0,Double or Twin Room,Multiple bed types,307.622,Singapore,Singapore,0,0,0,0,0,0,0,0
+Hotel Clover The Arts,"Boat Quay, Singapore",6.8,0.0,Executive Queen Room,1 queen bed,275.003,Singapore,Singapore,0,0,1,0,0,0,0,1
+Nostalgia Hotel,"Tiong Bahru, Singapore",7.7,8.0,Superior Double Room (No Window),1 full bed,257.181,Singapore,Singapore,0,1,0,0,0,0,0,1
+Heritage Collection on Chinatown - A Digital Hotel,"Chinatown, Singapore",8.8,9.1,Studio (No Windows),1 full bed,295.55400000000003,Singapore,Singapore,0,1,0,0,0,0,0,1
+Hotel Traveltine,"Kallang, Singapore",7.8,8.1,Superior Room,   1 double or 2 twins,331.128,Singapore,Singapore,2,0,0,0,0,0,0,2
+Perak Hotel,"Little India, Singapore",6.9,0.0,Superior Double or Twin Room,   1 double or 2 twins,270.04,Singapore,Singapore,2,0,0,0,0,0,0,2
+One Farrer Hotel,"Farrer Park, Singapore",8.6,9.1,Mint Room,Multiple bed types,574.078,Singapore,Singapore,0,0,0,0,0,0,0,0
+K Hotel 14,"Geylang, Singapore",4.9,0.0,Standard King Room,1 king bed,198.57500000000002,Singapore,Singapore,0,0,0,1,0,0,0,1
+K2 Guesthouse,"Queenstown, Singapore",6.5,0.0,Double Shared Bathroom,   1 double or 2 twins,262.597,Singapore,Singapore,2,0,0,0,0,0,0,2
+"Capri by Fraser China Square, Singapore","Chinatown, Singapore",8.5,9.3,Deluxe Double or Twin Room,1 twin bed,448.116,Singapore,Singapore,1,0,0,0,0,0,0,1
+JEN Singapore Tanglin by Shangri-La,"Orchard, Singapore",8.1,8.6,Club Double Room - Breakfast included,1 full bed,717.001,Singapore,Singapore,0,1,0,0,0,0,0,1
+Siloso Beach Resort - Sentosa,"Sentosa Island, Singapore",6.8,0.0,Deluxe Double or Twin Room,   1 double or 2 twins,464.836,Singapore,Singapore,2,0,0,0,0,0,0,2
+Aqueen Heritage Hotel Little India,"Farrer Park, Singapore",7.6,8.0,Cozy Room,1 queen bed,234.75900000000001,Singapore,Singapore,0,0,1,0,0,0,0,1
+Sofitel Singapore Sentosa Resort & Spa,"Sentosa Island, Singapore",7.3,0.0,"PRESTIGE SUITE, 1 King Size Bed",1 king bed,1180.8020000000001,Singapore,Singapore,0,0,0,1,0,0,0,1
+Orchard Point Serviced Apartments,"Orchard, Singapore",7.6,8.0,1 Studio Suite,1 full bed,401.86400000000003,Singapore,Singapore,0,1,0,0,0,0,0,1
+Louis Kienne Serviced Residences - Havelock,"Bukit Merah, Singapore",6.9,0.0,Deluxe King Studio,1 king bed,370.889,Singapore,Singapore,0,0,0,1,0,0,0,1
+30 Bencoolen,"Bencoolen, Singapore",8.5,8.9,Deluxe Twin Room,2 twin beds,333.817,Singapore,Singapore,2,0,0,0,0,0,0,2
+Hotel NuVe Stellar,"Lavender, Singapore",7.2,0.0,Superior Queen Room,1 queen bed,261.65,Singapore,Singapore,0,0,1,0,0,0,0,1
+The Great Madras by Hotel Calmo,"Little India, Singapore",5.6,0.0,The Great Suite,1 queen bed,353.951,Singapore,Singapore,0,0,1,0,0,0,0,1
+Citadines Connect City Centre Singapore,"Dhoby Ghaut, Singapore",8.5,8.9,Loft 1,1 queen bed,578.856,Singapore,Singapore,0,0,1,0,0,0,0,1
+Copthorne King's Hotel Singapore on Havelock,"Robertson Quay, Singapore",7.1,0.0,Deluxe Twin Room with Balcony,2 twin beds,351.135,Singapore,Singapore,2,0,0,0,0,0,0,2
+Santa Grand Hotel East Coast,"Katong, Singapore",7.9,8.4,Superior Double or Twin Room,   1 double or 2 twins,324.937,Singapore,Singapore,2,0,0,0,0,0,0,2
+Wink at Upper Cross Street,"Chinatown, Singapore",7.8,8.2,Bed in 4-Bed Mixed Dormitory Room,1 full bed,242.49800000000002,Singapore,Singapore,0,1,0,0,0,0,0,1
+Arton Boutique Hotel,"Lavender, Singapore",7.3,0.0,Superior Queen Room,1 queen bed,247.032,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel Grand Pacific,"Victoria, Singapore",6.9,0.0,Premier Double Room,1 queen bed,371.825,Singapore,Singapore,0,0,1,0,0,0,0,1
+ST Signature Jalan Besar,"Little India, Singapore",7.4,0.0,"Cabin M, Private Bathroom",1 full bed,317.903,Singapore,Singapore,0,1,0,0,0,0,0,1
+Harbour Ville Hotel - Hamilton,"Lavender, Singapore",6.4,0.0,Loft,1 king bed,316.332,Singapore,Singapore,0,0,0,1,0,0,0,1
+Lion Peak Hotel Raffles,"Bugis, Singapore",6.6,0.0,Premier King Room,1 king bed,455.69100000000003,Singapore,Singapore,0,0,0,1,0,0,0,1
+Novotel Living Singapore Orchard,"Orchard, Singapore",8.5,8.7,Executive Studio,,551.784,Singapore,Singapore,0,0,0,0,0,0,0,0
+The Daulat by Hotel Calmo,"Little India, Singapore",6.4,0.0,Loft Room,1 queen bed,323.121,Singapore,Singapore,0,0,1,0,0,0,0,1
+Heritage Collection on Boat Quay - South Bridge Wing,"Boat Quay, Singapore",8.7,8.6,Premium Studio with River View,1 queen bed,328.234,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel Royal,"Novena, Singapore",6.8,0.0,Deluxe Room ll,   1 double or 2 twins,454.80400000000003,Singapore,Singapore,2,0,0,0,0,0,0,2
+Hotel Clover 769 North Bridge Road,"Kampong Glam, Singapore",7.8,8.0,Executive King Room,1 king bed,296.419,Singapore,Singapore,0,0,0,1,0,0,0,1
+ST Signature Chinatown,"Chinatown, Singapore",7.2,0.0,"Cabin S, Double, Shared Bathroom",1 full bed,294.972,Singapore,Singapore,0,1,0,0,0,0,0,1
+Hotel Grand Central,"Orchard, Singapore",6.9,0.0,Executive Double or Twin Room,Multiple bed types,462.144,Singapore,Singapore,0,0,0,0,0,0,0,0
+Dao by Dorsett AMTD Singapore,"Marina Bay, Singapore",8.7,9.2,One-Bedroom Executive Suite,1 king bed,797.818,Singapore,Singapore,0,0,0,1,0,0,0,1
+Synergy B,"Queenstown, Singapore",9.4,8.5,Sunrise Cruise - 4 Hours (10am to 2pm),1 sofa bed,1433.2060000000001,Singapore,Singapore,0,0,0,0,0,0,0,0
+ST Signature Tanjong Pagar,"Chinatown, Singapore",7.7,8.1,"Cabin S, Double, Shared Bathroom",1 full bed,260.65500000000003,Singapore,Singapore,0,1,0,0,0,0,0,1
+Hotel NuVe Urbane,"Lavender, Singapore",7.8,8.3,Deluxe Queen Room,1 queen bed,326.045,Singapore,Singapore,0,0,1,0,0,0,0,1
+Oasia Residence Singapore,Singapore,9.0,9.1,Two-Bedroom Suite,2 king beds,1038.357,Singapore,Singapore,0,0,0,2,0,0,0,2
+Hotel Yan,"Lavender, Singapore",7.6,8.0,Deluxe King Room,1 queen bed,320.601,Singapore,Singapore,0,0,1,0,0,0,0,1
+Village Residence Hougang by Far East Hospitality,Singapore,8.2,8.5,Two-Bedroom Apartment,"3 beds (2 twins, 1 queen)",722.336,Singapore,Singapore,2,0,1,0,0,0,0,3
+Hi Hotel Dot,"Geylang, Singapore",6.5,0.0,Superior Double Room,1 queen bed,243.03300000000002,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hotel NuVe,"Kampong Glam, Singapore",7.0,0.0,NuVe Basic Room (No Window),1 queen bed,269.547,Singapore,Singapore,0,0,1,0,0,0,0,1
+Orchid Hotel,"Chinatown, Singapore",8.1,8.9,Deluxe Queen Room,1 queen bed,533.312,Singapore,Singapore,0,0,1,0,0,0,0,1
+Sandpiper Hotel,"Little India, Singapore",5.4,0.0,Deluxe Queen Room - With Window,1 full bed,309.254,Singapore,Singapore,0,1,0,0,0,0,0,1
+CUBE Boutique Capsule Hotel at Chinatown,"Chinatown, Singapore",7.7,8.0,Queen Capsule in Female Dormitory Room,1 queen bed,240.826,Singapore,Singapore,0,0,1,0,0,0,0,1
+Dash Living on Mackenzie,"Selegie, Singapore",7.4,0.0,Comfy Queen,1 queen bed,355.31600000000003,Singapore,Singapore,0,0,1,0,0,0,0,1
+Hi Hotel,"Kampong Glam, Singapore",6.1,0.0,Family Suite,1 king bed,439.354,Singapore,Singapore,0,0,0,1,0,0,0,1
+"Wanderlust, The Unlimited Collection by Oakwood","Little India, Singapore",8.9,9.3,Deluxe Room,1 full bed,429.173,Singapore,Singapore,0,1,0,0,0,0,0,1
+Hotel Fort Canning,"Downtown Singapore, Singapore",7.3,8.1,Studio Suite,1 king bed,1003.244,Singapore,Singapore,0,0,0,1,0,0,0,1
+Concorde Hotel Singapore,"Orchard, Singapore",7.5,0.0,Premier Club Double or Twin Room,Multiple bed types,658.717,Singapore,Singapore,0,0,0,0,0,0,0,0
+Amara Sanctuary Resort Sentosa,"Sentosa Island, Singapore",6.8,0.0,Deluxe Room,Multiple bed types,543.464,Singapore,Singapore,0,0,0,0,0,0,0,0
+Ambassador Transit Lounge Terminal 2,"Changi, Singapore",4.3,0.0,5 Hours Lounge Use Seated Sofa Chairs (NO BED) Check-in between 7AM to 7PM,,297.963,Singapore,Singapore,0,0,0,0,0,0,0,0
+Beary Best! Chinatown by a beary good hostel,"Chinatown, Singapore",5.9,0.0,Single Bed in 10-Bed Mixed Dormitory Room,•,120.604,Singapore,Singapore,0,0,0,0,0,0,0,0
+Bluewaters Pods 38 Hongkong St,"Boat Quay, Singapore",7.7,0.0,Single Bed in Mixed Dormitory Room,2 twin beds,150.964,Singapore,Singapore,2,0,0,0,0,0,0,2
+K Space Inn 14,"Boat Quay, Singapore",6.0,0.0,Single Space Pod,2 twin beds,151.347,Singapore,Singapore,2,0,0,0,0,0,0,2
+MET A Space Pod at Arab Street,"Kampong Glam, Singapore",7.7,0.0,Superior Single Space Pod,2 twin beds,170.62800000000001,Singapore,Singapore,2,0,0,0,0,0,0,2
+Ambassador Transit Lounge Terminal 3,"Changi, Singapore",6.8,0.0,5 Hours Lounge Use Seated Sofa Chairs (NO BED) Check-in between 7PM to 7AM,,306.993,Singapore,Singapore,0,0,0,0,0,0,0,0
+Coller Boutique Hostel,"Tiong Bahru, Singapore",7.9,0.0,Single Bed in Female Dormitory Room,2 twin beds,246.511,Singapore,Singapore,2,0,0,0,0,0,0,2
+Bluewaters Pods @ Bugis,"Bugis, Singapore",6.8,0.0,Standard Single Pod - Female Only,2 twin beds,145.55,Singapore,Singapore,2,0,0,0,0,0,0,2
+RedDoorz Premium at Balestier,"Novena, Singapore",7.1,0.0,Single Room,2 twin beds,368.509,Singapore,Singapore,2,0,0,0,0,0,0,2
+Circular House,"Boat Quay, Singapore",6.5,0.0,Superior Single Bed in Mixed Dorm,2 twin beds,209.678,Singapore,Singapore,2,0,0,0,0,0,0,2
+Beary Best! Kampong Glam,"Kampong Glam, Singapore",6.4,0.0,Lower Single Capsule - Mixed - Shared Bathroom,2 twin beds,118.712,Singapore,Singapore,2,0,0,0,0,0,0,2
+Ambassador Transit Hotel - Terminal 2,"Changi, Singapore",8.1,8.9,Single Room with Shared Bathroom (12 Hours Usage),2 twin beds,1177.14,Singapore,Singapore,2,0,0,0,0,0,0,2
+K2 Guesthouse Central,"Jalan Besar, Singapore",6.6,0.0,Single Bed in Dormitory Room,2 twin beds,227.40200000000002,Singapore,Singapore,2,0,0,0,0,0,0,2
+Heritage Collection on Boat Quay - Quayside Wing - A Digital Hotel,"Boat Quay, Singapore",8.6,9.0,Loft (No Windows),2 full beds,648.803,Singapore,Singapore,0,2,0,0,0,0,0,2
+KC Beach Club & Pool Villas,"Chaweng City Center , Chaweng",7.1,0.0,Double Room with Balcony and Sea View,1 queen bed,215.304,Chaweng,Thailand,0,0,1,0,0,0,0,1
+Combo Beach Hotel Samui,"Chaweng City Center , Chaweng",7.7,8.2,Deluxe Double Room,1 full bed,132.495,Chaweng,Thailand,0,1,0,0,0,0,0,1
+Lub d Koh Samui Chaweng Beach - SHA Extra Plus,"Chaweng City Center , Chaweng",8.6,9.1,Deluxe King Room (City),1 queen bed,132.453,Chaweng,Thailand,0,0,1,0,0,0,0,1
+Centara Reserve Samui - SHA Plus,"Chaweng City Center , Chaweng",9.4,9.6,Reserve Pool Suite,1 full bed,1314.063,Chaweng,Thailand,0,1,0,0,0,0,0,1
+Dara Samui Beach Resort Adult Only - SHA Extra Plus,"Chaweng City Center , Chaweng",8.0,8.5,Superior Double or Twin Room - Room Only,Multiple bed types,228.89600000000002,Chaweng,Thailand,0,0,0,0,0,0,0,0
+Mercure Samui Chaweng Tana,"Chaweng City Center , Chaweng",9.2,9.6,Deluxe Twin Room with Pool View,2 twin beds,199.866,Chaweng,Thailand,2,0,0,0,0,0,0,2
+Chaweng Regent Beach Resort - SHA Extra Plus,"Chaweng City Center , Chaweng",8.0,8.4,Deluxe Double or Twin Room,   1 double or 2 twins,280.259,Chaweng,Thailand,2,0,0,0,0,0,0,2
+OZO Chaweng Samui,"Chaweng City Center , Chaweng",8.3,8.8,Deluxe King,1 king bed,265.985,Chaweng,Thailand,0,0,0,1,0,0,0,1
+Montien House - SHA Plus,"Chaweng City Center , Chaweng",8.0,8.3,Superior Room with Garden View,Multiple bed types,148.93200000000002,Chaweng,Thailand,0,0,0,0,0,0,0,0
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diff --git a/main_page.py b/main_page.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdb2b9d29e925fe3ed4dcc07d352cb95495e645
--- /dev/null
+++ b/main_page.py
@@ -0,0 +1,84 @@
+import os
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+from st_pages import Page, show_pages
+from PIL import Image
+#from utils import authenticate_drive
+
+
+
+##################################################################################
+#                              PAGE CONFIGURATION                                #
+##################################################################################
+
+st.set_page_config(layout="wide")
+
+
+
+
+##################################################################################
+#                              GOOGLE DRIVE CONNEXION                            #
+##################################################################################
+
+# if ["drive_oauth"] not in st.session_state:
+#     st.session_state["drive_oauth"] = authenticate_drive()
+
+# drive_oauth = st.session_state["drive_oauth"]
+
+
+
+
+##################################################################################
+#                                   TITLE                                        #
+##################################################################################
+
+st.image("images/AI.jpg")
+st.title("AI and Data Science Examples")
+st.subheader("HEC Paris, 2023-2024")
+st.markdown("Course provided by **Shirish C. SRIVASTAVA**")
+
+st.markdown(" ")
+st.info("""**About the app**: The AI and Data Science Examples app was created to introduce students to the field of Data Science by showcasing real-life applications of AI.
+        It includes use cases using traditional Machine Learning algorithms on structured data, as well as Deep Learning models run on unstructured data (text, images,...).""")
+
+st.divider()
+
+
+#Hi! PARIS collaboration mention
+st.markdown("  ")
+image_hiparis = Image.open('images/hi-paris.png')
+st.image(image_hiparis, width=150)
+url = "https://www.hi-paris.fr/"
+st.markdown("**The app was made in collaboration with: [Hi! PARIS Engineering Team](%s)**" % url)
+
+
+
+
+##################################################################################
+#                              DASHBOARD/SIDEBAR                                 #
+##################################################################################
+
+
+# AI use case pages
+show_pages(
+    [
+        Page("main_page.py", "Home Page", "🏠"),
+        Page("pages/supervised_unsupervised_page.py", "Supervised vs Unsupervised", "🔍"), 
+        Page("pages/timeseries_analysis.py", "Time Series Forecasting", "📈"),
+        Page("pages/sentiment_analysis.py", "Sentiment Analysis", "👍"),
+        #Page("pages/object_detection.py", "Object Detection", "📹"), #need to reduce RAM costs
+        Page("pages/recommendation_system.py", "Recommendation system", "🛒")
+    ]
+)
+
+
+
+##################################################################################
+#                               PAGE CONTENT                                     #
+##################################################################################
+
+
+
+
diff --git a/notebooks/Supervised-Unsupervised/credit_score.ipynb b/notebooks/Supervised-Unsupervised/credit_score.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a05485b4bf9e47aa07b69239d6e81355018b5cc
--- /dev/null
+++ b/notebooks/Supervised-Unsupervised/credit_score.ipynb
@@ -0,0 +1,1707 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\credit_score.csv\"\n",
+    "credit_data = pd.read_csv(path_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Credit score classification \n",
+    "\n",
+    "https://www.kaggle.com/datasets/parisrohan/credit-score-classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_loan_clean = []\n",
+    "for text in list(credit_data[\"Type_of_Loan\"].unique()):\n",
+    "    text = text.replace(\",\",\" \")\n",
+    "    text = text.replace(\"loan\",\"\")\n",
+    "    text = text.replace(\"not specified\",\"\")\n",
+    "    text = text.replace(\"No Data\",\"\")\n",
+    "    text = text.replace(\"  \",\" \")\n",
+    "    text = text.strip()\n",
+    "    \n",
+    "    type_loan_clean.append(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_type_loan = \" \".join(type_loan_clean)\n",
+    "types_loan = list(set([loan for loan in all_type_loan.split(\" \") if loan !=\"\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for loan in types_loan:\n",
+    "    credit_data[f\"{loan.capitalize()}Loan\"] = credit_data[\"Type_of_Loan\"].apply(lambda x: 1 if loan in x else 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean data\n",
+    "credit_data.drop(columns=[\"SSN\",\"Name\",\"Month\",\"ID\",\"Type_of_Loan\",\"Payment_Behaviour\"], inplace=True)\n",
+    "credit_data = credit_data.loc[~credit_data[\"Occupation\"].isin([\"Media_Manager\",\"Manager\"])]\n",
+    "credit_data[\"Age\"] = credit_data[\"Age\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group customer data for numerical variables (mean)\n",
+    "num_columns = list(credit_data.select_dtypes(include=[float]).columns)\n",
+    "credit_data_num = credit_data[[\"Customer_ID\"] + num_columns].groupby(\"Customer_ID\").median().reset_index().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group customer data for categorical variables (mode)\n",
+    "cat_columns = list(credit_data.select_dtypes(include=[int,object]).columns)\n",
+    "credit_data_cat = credit_data[cat_columns].groupby(\"Customer_ID\").apply(lambda group: group.mode().iloc[0] if not group.mode().empty else pd.Series(index=group.columns))\n",
+    "credit_data_cat = credit_data_cat.drop(columns=[\"Customer_ID\"]).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge numerical and categorical merged datasets\n",
+    "credit_data_clean = credit_data_cat.merge(credit_data_num, on=\"Customer_ID\", how=\"inner\") # merge cleaned data for numerical and categorical variables\n",
+    "#credit_data_clean = credit_data_clean.sample(5000, random_state=0) # select 2000 samples randomly\n",
+    "credit_data_clean.reset_index(drop=True, inplace=True)\n",
+    "credit_data_clean.drop(columns=[\"Customer_ID\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "credit_data_clean = credit_data_clean.loc[credit_data_clean[\"Age\"]>18]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Occupation</th>\n",
+       "      <th>Credit_Mix</th>\n",
+       "      <th>Payment_of_Min_Amount</th>\n",
+       "      <th>Credit_Score</th>\n",
+       "      <th>HomeLoan</th>\n",
+       "      <th>Credit-builderLoan</th>\n",
+       "      <th>MortgageLoan</th>\n",
+       "      <th>ConsolidationLoan</th>\n",
+       "      <th>EquityLoan</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Delay_from_due_date</th>\n",
+       "      <th>Num_of_Delayed_Payment</th>\n",
+       "      <th>Changed_Credit_Limit</th>\n",
+       "      <th>Num_Credit_Inquiries</th>\n",
+       "      <th>Outstanding_Debt</th>\n",
+       "      <th>Credit_Utilization_Ratio</th>\n",
+       "      <th>Credit_History_Age</th>\n",
+       "      <th>Total_EMI_per_month</th>\n",
+       "      <th>Amount_invested_monthly</th>\n",
+       "      <th>Monthly_Balance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>Journalist</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1942.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>186.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>317.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1139.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>362.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>Accountant</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>982.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>334.0</td>\n",
+       "      <td>188.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>567.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Writer</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1372.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>182.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>469.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>Mechanic</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Standard</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1071.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>314.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>400.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age  Occupation Credit_Mix Payment_of_Min_Amount Credit_Score  HomeLoan  \\\n",
+       "0  38.0  Journalist   Standard                   Yes         Poor       0.0   \n",
+       "1  37.0   Developer   Standard                   Yes     Standard       0.0   \n",
+       "2  22.0  Accountant   Standard                   Yes     Standard       0.0   \n",
+       "3  44.0      Writer   Standard                   Yes     Standard       0.0   \n",
+       "4  30.0    Mechanic       Good                    No     Standard       1.0   \n",
+       "\n",
+       "   Credit-builderLoan  MortgageLoan  ConsolidationLoan  EquityLoan  ...  \\\n",
+       "0                 1.0           0.0                0.0         0.0  ...   \n",
+       "1                 0.0           0.0                0.0         0.0  ...   \n",
+       "2                 1.0           0.0                0.0         0.0  ...   \n",
+       "3                 0.0           0.0                0.0         0.0  ...   \n",
+       "4                 0.0           1.0                0.0         1.0  ...   \n",
+       "\n",
+       "   Delay_from_due_date  Num_of_Delayed_Payment  Changed_Credit_Limit  \\\n",
+       "0                 48.0                    12.0                  11.0   \n",
+       "1                 26.0                    11.0                  14.0   \n",
+       "2                 19.0                    18.0                  17.0   \n",
+       "3                 20.0                    14.0                  15.0   \n",
+       "4                  9.0                     2.0                   3.0   \n",
+       "\n",
+       "   Num_Credit_Inquiries  Outstanding_Debt  Credit_Utilization_Ratio  \\\n",
+       "0                   8.0            1942.0                      33.0   \n",
+       "1                  10.0            1139.0                      28.0   \n",
+       "2                   7.0             982.0                      36.0   \n",
+       "3                   6.0            1372.0                      35.0   \n",
+       "4                   1.0            1071.0                      33.0   \n",
+       "\n",
+       "   Credit_History_Age  Total_EMI_per_month  Amount_invested_monthly  \\\n",
+       "0               186.0                 27.0                     45.0   \n",
+       "1               296.0                  0.0                     51.0   \n",
+       "2               334.0                188.0                    101.0   \n",
+       "3               182.0                258.0                    102.0   \n",
+       "4               314.0                 62.0                     53.0   \n",
+       "\n",
+       "   Monthly_Balance  \n",
+       "0            317.0  \n",
+       "1            362.0  \n",
+       "2            567.0  \n",
+       "3            469.0  \n",
+       "4            400.0  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "credit_data_clean.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "sns.heatmap(credit_data_clean[num_columns].corr(), annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drop_corr_columns = [\"Monthly_Inhand_Salary\",\"Amount_invested_monthly\", \"Num_Credit_Inquiries\", 'Credit_Utilization_Ratio', 'Total_EMI_per_month', 'Monthly_Balance']\n",
+    "credit_data_clean.drop(columns=drop_corr_columns, inplace=True)\n",
+    "num_columns_clean = [col for col in num_columns if col not in drop_corr_columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Annual_Income</th>\n",
+       "      <th>Num_Bank_Accounts</th>\n",
+       "      <th>Num_Credit_Card</th>\n",
+       "      <th>Interest_Rate</th>\n",
+       "      <th>Num_of_Loan</th>\n",
+       "      <th>Delay_from_due_date</th>\n",
+       "      <th>Num_of_Delayed_Payment</th>\n",
+       "      <th>Changed_Credit_Limit</th>\n",
+       "      <th>Outstanding_Debt</th>\n",
+       "      <th>Credit_History_Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "      <td>9943.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>51265.81</td>\n",
+       "      <td>5.28</td>\n",
+       "      <td>5.48</td>\n",
+       "      <td>14.14</td>\n",
+       "      <td>3.43</td>\n",
+       "      <td>20.51</td>\n",
+       "      <td>13.06</td>\n",
+       "      <td>10.18</td>\n",
+       "      <td>1379.43</td>\n",
+       "      <td>225.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>38778.67</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>2.04</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>2.42</td>\n",
+       "      <td>14.53</td>\n",
+       "      <td>6.21</td>\n",
+       "      <td>6.45</td>\n",
+       "      <td>1136.02</td>\n",
+       "      <td>99.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>7006.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19594.50</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>543.00</td>\n",
+       "      <td>152.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37580.00</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>12.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>17.00</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>1123.00</td>\n",
+       "      <td>224.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>72899.50</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>20.00</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>27.00</td>\n",
+       "      <td>18.00</td>\n",
+       "      <td>14.00</td>\n",
+       "      <td>1823.00</td>\n",
+       "      <td>306.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>179987.00</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>11.00</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>62.00</td>\n",
+       "      <td>25.00</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>4998.00</td>\n",
+       "      <td>400.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Annual_Income  Num_Bank_Accounts  Num_Credit_Card  Interest_Rate  \\\n",
+       "count        9943.00            9943.00          9943.00        9943.00   \n",
+       "mean        51265.81               5.28             5.48          14.14   \n",
+       "std         38778.67               2.60             2.04           8.69   \n",
+       "min          7006.00               0.00             0.00           1.00   \n",
+       "25%         19594.50               3.00             4.00           7.00   \n",
+       "50%         37580.00               5.00             5.00          12.00   \n",
+       "75%         72899.50               7.00             7.00          20.00   \n",
+       "max        179987.00              10.00            11.00          34.00   \n",
+       "\n",
+       "       Num_of_Loan  Delay_from_due_date  Num_of_Delayed_Payment  \\\n",
+       "count      9943.00              9943.00                 9943.00   \n",
+       "mean          3.43                20.51                   13.06   \n",
+       "std           2.42                14.53                    6.21   \n",
+       "min           0.00                 0.00                    0.00   \n",
+       "25%           2.00                10.00                    9.00   \n",
+       "50%           3.00                17.00                   13.00   \n",
+       "75%           5.00                27.00                   18.00   \n",
+       "max           9.00                62.00                   25.00   \n",
+       "\n",
+       "       Changed_Credit_Limit  Outstanding_Debt  Credit_History_Age  \n",
+       "count               9943.00           9943.00             9943.00  \n",
+       "mean                  10.18           1379.43              225.71  \n",
+       "std                    6.45           1136.02               99.17  \n",
+       "min                    0.00              0.00                4.00  \n",
+       "25%                    5.00            543.00              152.00  \n",
+       "50%                    9.00           1123.00              224.00  \n",
+       "75%                   14.00           1823.00              306.00  \n",
+       "max                   30.00           4998.00              400.00  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Identify possible outliers\n",
+    "credit_data_clean[num_columns_clean].describe().round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,12))\n",
+    "for i,col in enumerate(credit_data_clean[num_columns_clean].columns):\n",
+    "    plt.subplot(4,3,i+1)\n",
+    "    sns.boxplot(data=credit_data_clean, x=col, hue=\"Credit_Score\", fill=\"Credit_Score\")\n",
+    "    plt.xlabel(\"\")\n",
+    "    plt.ylabel(\"\")\n",
+    "    plt.title(str(col))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x1400 with 11 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exploratory Data Analysis on numerical variables\n",
+    "plt.figure(figsize=(22,14))\n",
+    "for i,col in enumerate(list(credit_data_clean[num_columns_clean].columns) + [\"Age\"]):\n",
+    "    plt.subplot(4,3,i+1)\n",
+    "    sns.kdeplot(data=credit_data_clean, x=col, hue=\"Credit_Score\", fill=\"Credit_Score\")\n",
+    "    plt.xlabel(\"\")\n",
+    "    plt.ylabel(\"\")\n",
+    "    plt.title(str(col))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Credit Mix', ylabel='value'>"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "credit_occupation_df = pd.crosstab(credit_data_clean[\"Credit_Mix\"], \n",
+    "                                   credit_data_clean[\"Credit_Score\"], normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n",
+    "credit_occupation_df.rename_axis(None, axis=1, inplace=True)\n",
+    "credit_occupation_df = credit_occupation_df.melt(id_vars=[\"Credit_Mix\"])\n",
+    "credit_occupation_df.rename({\"variable\":\"Credit Score\", \"Credit_Mix\":\"Credit Mix\"}, axis=1, inplace=True)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(data = credit_occupation_df, x=\"Credit Mix\", y=\"value\", hue=\"Credit Score\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "credit_data_clean.drop(columns=[col for col in cat_columns if \"Loan\" in col], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = credit_data_clean.drop(columns=[\"Credit_Score\"])\n",
+    "y = credit_data_clean[\"Credit_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save training data for app\n",
+    "X_train_save = X_train.copy()\n",
+    "X_train_save[\"Credit_Score\"] = y_train\n",
+    "\n",
+    "path_savedata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\credit_score\"\n",
+    "X_train_save.to_pickle(os.path.join(path_savedata,\"credit_score_train_raw.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save test data for app\n",
+    "X_test_save = X_test.copy()\n",
+    "X_test_save.to_pickle(os.path.join(path_savedata,\"credit_score_test_raw.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, OneHotEncoder\n",
+    "\n",
+    "cat_columns_clean = [col for col in cat_columns if col not in [\"Customer_ID\",\"Credit_Score\",\"Age\"]]\n",
+    "cat_columns_clean = [col for col in cat_columns_clean if \"Loan\" not in col]\n",
+    "num_columns_clean.append(\"Age\")\n",
+    "\n",
+    "# Build data processing pipeline\n",
+    "ct = ColumnTransformer(\n",
+    "    [(\"numerical\", RobustScaler(), num_columns_clean), \n",
+    "     (\"categorical\", OneHotEncoder(sparse_output=False), cat_columns_clean)],\n",
+    "     remainder='passthrough')\n",
+    "\n",
+    "X_train_pp = ct.fit_transform(X_train)\n",
+    "X_test_pp = ct.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Annual_Income</th>\n",
+       "      <th>Num_Bank_Accounts</th>\n",
+       "      <th>Num_Credit_Card</th>\n",
+       "      <th>Interest_Rate</th>\n",
+       "      <th>Num_of_Loan</th>\n",
+       "      <th>Delay_from_due_date</th>\n",
+       "      <th>Num_of_Delayed_Payment</th>\n",
+       "      <th>Changed_Credit_Limit</th>\n",
+       "      <th>Outstanding_Debt</th>\n",
+       "      <th>Credit_History_Age</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Occupation_Musician</th>\n",
+       "      <th>Occupation_Scientist</th>\n",
+       "      <th>Occupation_Teacher</th>\n",
+       "      <th>Occupation_Writer</th>\n",
+       "      <th>Credit_Mix_Bad</th>\n",
+       "      <th>Credit_Mix_Good</th>\n",
+       "      <th>Credit_Mix_Standard</th>\n",
+       "      <th>Payment_of_Min_Amount_NM</th>\n",
+       "      <th>Payment_of_Min_Amount_No</th>\n",
+       "      <th>Payment_of_Min_Amount_Yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.472788</td>\n",
+       "      <td>-1.25</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>-0.846154</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>-0.388889</td>\n",
+       "      <td>-0.111111</td>\n",
+       "      <td>-0.777778</td>\n",
+       "      <td>-0.640219</td>\n",
+       "      <td>1.116883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.074266</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>-0.307692</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.388889</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.444444</td>\n",
+       "      <td>-0.639437</td>\n",
+       "      <td>-0.051948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.693351</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.666667</td>\n",
+       "      <td>-0.384615</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>1.222222</td>\n",
+       "      <td>-0.727770</td>\n",
+       "      <td>-0.129870</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.039409</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.692308</td>\n",
+       "      <td>-0.666667</td>\n",
+       "      <td>-0.388889</td>\n",
+       "      <td>-1.333333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.314247</td>\n",
+       "      <td>0.389610</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.402867</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.333333</td>\n",
+       "      <td>1.538462</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.944444</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>1.212429</td>\n",
+       "      <td>-0.961039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Annual_Income  Num_Bank_Accounts  Num_Credit_Card  Interest_Rate  \\\n",
+       "0       0.472788              -1.25        -0.333333      -0.846154   \n",
+       "1      -0.074266              -0.25        -0.333333      -0.307692   \n",
+       "2       1.693351              -0.50        -0.666667      -0.384615   \n",
+       "3      -0.039409               0.00         0.000000      -0.692308   \n",
+       "4       0.402867               1.00         1.333333       1.538462   \n",
+       "\n",
+       "   Num_of_Loan  Delay_from_due_date  Num_of_Delayed_Payment  \\\n",
+       "0    -0.333333            -0.388889               -0.111111   \n",
+       "1    -1.000000            -0.388889               -1.000000   \n",
+       "2    -1.000000             0.277778                0.111111   \n",
+       "3    -0.666667            -0.388889               -1.333333   \n",
+       "4     2.000000             0.944444                0.444444   \n",
+       "\n",
+       "   Changed_Credit_Limit  Outstanding_Debt  Credit_History_Age  ...  \\\n",
+       "0             -0.777778         -0.640219            1.116883  ...   \n",
+       "1             -0.444444         -0.639437           -0.051948  ...   \n",
+       "2              1.222222         -0.727770           -0.129870  ...   \n",
+       "3              0.000000         -0.314247            0.389610  ...   \n",
+       "4              0.777778          1.212429           -0.961039  ...   \n",
+       "\n",
+       "   Occupation_Musician  Occupation_Scientist  Occupation_Teacher  \\\n",
+       "0                  0.0                   0.0                 0.0   \n",
+       "1                  0.0                   0.0                 0.0   \n",
+       "2                  0.0                   0.0                 0.0   \n",
+       "3                  0.0                   0.0                 0.0   \n",
+       "4                  1.0                   0.0                 0.0   \n",
+       "\n",
+       "   Occupation_Writer  Credit_Mix_Bad  Credit_Mix_Good  Credit_Mix_Standard  \\\n",
+       "0                0.0             0.0              1.0                  0.0   \n",
+       "1                0.0             0.0              1.0                  0.0   \n",
+       "2                0.0             0.0              0.0                  1.0   \n",
+       "3                1.0             0.0              1.0                  0.0   \n",
+       "4                0.0             1.0              0.0                  0.0   \n",
+       "\n",
+       "   Payment_of_Min_Amount_NM  Payment_of_Min_Amount_No  \\\n",
+       "0                       0.0                       1.0   \n",
+       "1                       0.0                       1.0   \n",
+       "2                       0.0                       0.0   \n",
+       "3                       0.0                       1.0   \n",
+       "4                       0.0                       0.0   \n",
+       "\n",
+       "   Payment_of_Min_Amount_Yes  \n",
+       "0                        0.0  \n",
+       "1                        0.0  \n",
+       "2                        1.0  \n",
+       "3                        0.0  \n",
+       "4                        1.0  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
+    "df_clean = pd.DataFrame(X_train_pp, columns=columns_transform)\n",
+    "df_clean.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_save_pp = X_test_pp.copy()\n",
+    "X_test_save_pp = pd.DataFrame(X_test_save_pp, columns=columns_transform)\n",
+    "X_test_save_pp.to_pickle(os.path.join(path_savedata,\"credit_score_test_pp.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001206 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
+      "[LightGBM] [Info] Total Bins 968\n",
+      "[LightGBM] [Info] Number of data points in the train set: 6960, number of used features: 30\n",
+      "[LightGBM] [Info] Start training from score -1.553557\n",
+      "[LightGBM] [Info] Start training from score -1.134150\n",
+      "[LightGBM] [Info] Start training from score -0.761832\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LGBMClassifier()"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import lightgbm as lgb\n",
+    "\n",
+    "params = {\n",
+    "'boosting_type': 'gbdt',\n",
+    "'num_leaves': 30,\n",
+    "'learning_rate': 0.05,\n",
+    "'feature_fraction': 0.9\n",
+    "}\n",
+    "\n",
+    "clf = lgb.LGBMClassifier()\n",
+    "clf.fit(X_train_pp, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy 0.8900862068965517\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "y_pred_lgb_train = clf.predict(X_train_pp)\n",
+    "print(\"Accuracy\", accuracy_score(y_train, y_pred_lgb_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy 0.7525980556486759\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_lgb_test = clf.predict(X_test_pp)\n",
+    "print(\"Accuracy\", accuracy_score(y_test, y_pred_lgb_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x29b6a81e790>"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "cm = confusion_matrix(y_test, y_pred_lgb_test, normalize=\"true\")\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.8853448275862069\n",
+      "Test Accuracy 0.7509218907140462\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
+    "\n",
+    "rf = RandomForestClassifier(max_depth=14, class_weight=\"balanced_subsample\", n_estimators=600)\n",
+    "rf.fit(X_train_pp, y_train)\n",
+    "\n",
+    "y_pred_rf_train = rf.predict(X_train_pp)\n",
+    "y_pred_rf_test = rf.predict(X_test_pp)\n",
+    "\n",
+    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_rf_train))\n",
+    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_rf_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train_results = pd.DataFrame({\"y_pred\":y_pred_rf_train, \n",
+    "                                 \"y_true\":y_train})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x29b0a181be0>"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_train, y_pred_rf_train, normalize=\"true\")\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_cm = pd.DataFrame(cm, columns=[\"Good\",\"Poor\",\"Standard\"])\n",
+    "#df_cm.insert(0,\"Label\",[\"Good\",\"Poor\",\"Standard\"])\n",
+    "df_cm.to_pickle(os.path.join(path_savedata,\"credit_score_cm_train\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "path_model = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\pretrained_models\\supervised_learning\"\n",
+    "with open(os.path.join(path_model,'credit_score_model.pkl'),'wb') as f:\n",
+    "    pickle.dump(rf,f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_creditscore = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\credit_score\"\n",
+    "\n",
+    "test_raw = pd.read_pickle(os.path.join(path_creditscore, \"credit_score_test_raw.pkl\")) \n",
+    "test_pp = pd.read_pickle(os.path.join(path_creditscore, \"credit_score_test_pp.pkl\")) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_raw = test_raw[:1000]\n",
+    "test_pp = test_pp[:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_savedata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\credit_score\"\n",
+    "test_raw.to_pickle(os.path.join(path_savedata,\"credit_score_test_raw.pkl\"))\n",
+    "test_pp.to_pickle(os.path.join(path_savedata,\"credit_score_test_pp.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 30)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_pp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv-app-ai-hec",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Supervised-Unsupervised/customer_churn.ipynb b/notebooks/Supervised-Unsupervised/customer_churn.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..975feb69ffe060e66e715b61819e6efe7ff628d8
--- /dev/null
+++ b/notebooks/Supervised-Unsupervised/customer_churn.ipynb
@@ -0,0 +1,1947 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Telco customer churn \n",
+    "\n",
+    "https://www.kaggle.com/datasets/blastchar/telco-customer-churn\n",
+    "\n",
+    "- **customerID**: Customer ID\n",
+    "- **gender**: Whether the customer is a male or a femal\n",
+    "- **SeniorCitizen**: Whether the customer is a senior citizen or not (1, 0)\n",
+    "- **Partner**: Whether the customer has a partner or not (Yes, No)\n",
+    "- **Dependents**: Whether the customer has dependents or not (Yes, No)\n",
+    "- **tenure**: Number of months the customer has stayed with the company\n",
+    "- **PhoneService**: Whether the customer has a phone service or not (Yes, No)\n",
+    "- **MultipleLines**: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
+    "- **InternetService**: Customer’s internet service provider (DSL, Fiber optic, No)\n",
+    "- **OnlineSecurity**: Whether the customer has online security or not (Yes, No, No internet service)\n",
+    "- **OnlineBackup**: Whether the customer has online backup or not (Yes, No, No internet service)\n",
+    "- **DeviceProtection**: Whether the customer has device protection or not (Yes, No, No internet service)\n",
+    "- **TechSupport**: Whether the customer has tech support or not (Yes, No, No internet service)\n",
+    "- **StreamingTV**: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
+    "- **StreamingMovies**: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
+    "- **Contract**: The contract term of the customer (Month-to-month, One year, Two year)\n",
+    "- **PaperlessBilling**: Whether the customer has paperless billing or not (Yes, No)\n",
+    "- **PaymentMethod**: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
+    "- **MonthlyCharges**: The amount charged to the customer monthly\n",
+    "- **TotalCharges**: The total amount charged to the customer\n",
+    "- **Churn**: Whether the customer churned or not (Yes or No)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\telco-customer-churn.csv\"\n",
+    "churn_data = pd.read_csv(path_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7043 entries, 0 to 7042\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   customerID        7043 non-null   object \n",
+      " 1   gender            7043 non-null   object \n",
+      " 2   SeniorCitizen     7043 non-null   int64  \n",
+      " 3   Partner           7043 non-null   object \n",
+      " 4   Dependents        7043 non-null   object \n",
+      " 5   tenure            7043 non-null   int64  \n",
+      " 6   PhoneService      7043 non-null   object \n",
+      " 7   MultipleLines     7043 non-null   object \n",
+      " 8   InternetService   7043 non-null   object \n",
+      " 9   OnlineSecurity    7043 non-null   object \n",
+      " 10  OnlineBackup      7043 non-null   object \n",
+      " 11  DeviceProtection  7043 non-null   object \n",
+      " 12  TechSupport       7043 non-null   object \n",
+      " 13  StreamingTV       7043 non-null   object \n",
+      " 14  StreamingMovies   7043 non-null   object \n",
+      " 15  Contract          7043 non-null   object \n",
+      " 16  PaperlessBilling  7043 non-null   object \n",
+      " 17  PaymentMethod     7043 non-null   object \n",
+      " 18  MonthlyCharges    7043 non-null   float64\n",
+      " 19  TotalCharges      7043 non-null   object \n",
+      " 20  Churn             7043 non-null   object \n",
+      "dtypes: float64(1), int64(2), object(18)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "churn_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data.drop(columns=[\"gender\",\"customerID\"],inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data[\"MultipleLines\"] = churn_data[\"MultipleLines\"].apply(lambda x: 0 if x in [\"No\",\"No phone service\"] else 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in [\"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\"]:\n",
+    "    churn_data[col] = churn_data[col].apply(lambda x: 0 if x in [\"No internet service\", \"No\"] else 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binary_columns = [\"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\", \"Churn\"]\n",
+    "for col in binary_columns:\n",
+    "    churn_data[col] = churn_data[col].map({\"No\":0, \"Yes\":1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data[\"TotalCharges\"] = churn_data[\"TotalCharges\"].apply(lambda x: np.nan if x==' ' else x).astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='TotalCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data=churn_data, x=\"TotalCharges\", hue=\"Churn\", fill=\"Churn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1600 with 15 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exploratory Data Analysis on categorical variables\n",
+    "\n",
+    "cat_columns_plot = [col for col in churn_data.select_dtypes(include=[\"object\",\"int\"]).columns if col not in [\"Churn\",\"tenure\"]]\n",
+    "\n",
+    "plt.figure(figsize=(16,16))\n",
+    "for i,col in enumerate(cat_columns_plot):\n",
+    "    plt.subplot(5,3,i+1)\n",
+    "\n",
+    "    df_plot = pd.crosstab(churn_data[col], churn_data[\"Churn\"].map({0:\"No Churn\", 1:\"Churn\"}), normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n",
+    "    df_plot.rename_axis(None, axis=1, inplace=True)\n",
+    "    df_plot = df_plot.melt(id_vars=[col])\n",
+    "\n",
+    "    sns.barplot(data = df_plot, x=col, y=\"value\", hue=\"variable\")\n",
+    "    plt.xlabel(\"\")\n",
+    "    plt.ylabel(\"\")\n",
+    "    plt.title(str(col))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = churn_data.drop(columns=[\"Churn\"])\n",
+    "y = churn_data[\"Churn\"]\n",
+    "\n",
+    "# Split train/test set\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, OneHotEncoder\n",
+    "\n",
+    "num_columns = list(churn_data.select_dtypes(include=[\"float\"]).columns)\n",
+    "num_columns.append(\"tenure\")\n",
+    "\n",
+    "cat_columns = churn_data.select_dtypes(include=[\"object\"]).columns\n",
+    "\n",
+    "# Build data processing pipeline\n",
+    "ct = ColumnTransformer(\n",
+    "    [(\"numerical\", MinMaxScaler(), num_columns), \n",
+    "     (\"categorical\", OneHotEncoder(sparse_output=False), cat_columns)], \n",
+    "      remainder='passthrough')\n",
+    "\n",
+    "X_train_pp = ct.fit_transform(X_train)\n",
+    "X_test_pp = ct.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy 0.8759318423855165\n",
+      "Test Accuracy 0.7757274662881476\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
+    "\n",
+    "rf = RandomForestClassifier(max_depth=12, class_weight=\"balanced_subsample\")\n",
+    "rf.fit(X_train_pp, y_train)\n",
+    "\n",
+    "y_pred_train = rf.predict(X_train_pp)\n",
+    "y_pred_test = rf.predict(X_test_pp)\n",
+    "\n",
+    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_train))\n",
+    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x255137ceb20>"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "cm = confusion_matrix(y_test, y_pred_test, normalize=\"true\")*100\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf.classes_)\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf.feature_importances_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LightGBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
+      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
+      "[LightGBM] [Info] Number of positive: 1495, number of negative: 4139\n",
+      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000387 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 627\n",
+      "[LightGBM] [Info] Number of data points in the train set: 5634, number of used features: 25\n",
+      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265353 -> initscore=-1.018328\n",
+      "[LightGBM] [Info] Start training from score -1.018328\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import lightgbm as lgb\n",
+    "\n",
+    "params = {\n",
+    "'boosting_type': 'gbdt',\n",
+    "'num_leaves': 30,\n",
+    "'learning_rate': 0.05,\n",
+    "'feature_fraction': 0.9\n",
+    "}\n",
+    "\n",
+    "clf = lgb.LGBMClassifier(**params)\n",
+    "clf.fit(X_train_pp, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
+      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
+      "Train Accuracy 0.8473553425630103\n",
+      "Test Accuracy 0.7913413768630234\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_lgb_train = clf.predict(X_train_pp)\n",
+    "y_pred_lgb_test = clf.predict(X_test_pp)\n",
+    "\n",
+    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_lgb_train))\n",
+    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_lgb_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='importance', ylabel='variable'>"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
+    "\n",
+    "df_features_imp = pd.DataFrame({\"variable\":columns_transform,\n",
+    "                               \"importance\":clf.feature_importances_})\n",
+    "\n",
+    "sns.barplot(data=df_features_imp, x=\"importance\", y=\"variable\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CatBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = churn_data.drop(columns=[\"Churn\"])\n",
+    "y = churn_data[\"Churn\"]\n",
+    "\n",
+    "# Split train/test set\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
+    "\n",
+    "# Split train/validation set\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate set to 0.048569\n",
+      "0:\tlearn: 0.6627881\ttest: 0.6623821\tbest: 0.6623821 (0)\ttotal: 32.5ms\tremaining: 32.5s\n",
+      "1:\tlearn: 0.6372805\ttest: 0.6359148\tbest: 0.6359148 (1)\ttotal: 62.2ms\tremaining: 31.1s\n",
+      "2:\tlearn: 0.6118743\ttest: 0.6094715\tbest: 0.6094715 (2)\ttotal: 97.7ms\tremaining: 32.5s\n",
+      "3:\tlearn: 0.5898587\ttest: 0.5870121\tbest: 0.5870121 (3)\ttotal: 123ms\tremaining: 30.6s\n",
+      "4:\tlearn: 0.5729321\ttest: 0.5694306\tbest: 0.5694306 (4)\ttotal: 141ms\tremaining: 28.1s\n",
+      "5:\tlearn: 0.5563865\ttest: 0.5527201\tbest: 0.5527201 (5)\ttotal: 166ms\tremaining: 27.5s\n",
+      "6:\tlearn: 0.5406930\ttest: 0.5370096\tbest: 0.5370096 (6)\ttotal: 194ms\tremaining: 27.5s\n",
+      "7:\tlearn: 0.5268732\ttest: 0.5229710\tbest: 0.5229710 (7)\ttotal: 223ms\tremaining: 27.7s\n",
+      "8:\tlearn: 0.5149236\ttest: 0.5106099\tbest: 0.5106099 (8)\ttotal: 255ms\tremaining: 28.1s\n",
+      "9:\tlearn: 0.5067475\ttest: 0.5020982\tbest: 0.5020982 (9)\ttotal: 271ms\tremaining: 26.9s\n",
+      "10:\tlearn: 0.4972163\ttest: 0.4926949\tbest: 0.4926949 (10)\ttotal: 301ms\tremaining: 27s\n",
+      "11:\tlearn: 0.4896058\ttest: 0.4852133\tbest: 0.4852133 (11)\ttotal: 327ms\tremaining: 26.9s\n",
+      "12:\tlearn: 0.4825590\ttest: 0.4780927\tbest: 0.4780927 (12)\ttotal: 357ms\tremaining: 27.1s\n",
+      "13:\tlearn: 0.4756378\ttest: 0.4711545\tbest: 0.4711545 (13)\ttotal: 383ms\tremaining: 27s\n",
+      "14:\tlearn: 0.4698411\ttest: 0.4662418\tbest: 0.4662418 (14)\ttotal: 410ms\tremaining: 26.9s\n",
+      "15:\tlearn: 0.4652496\ttest: 0.4619031\tbest: 0.4619031 (15)\ttotal: 438ms\tremaining: 27s\n",
+      "16:\tlearn: 0.4611658\ttest: 0.4577089\tbest: 0.4577089 (16)\ttotal: 465ms\tremaining: 26.9s\n",
+      "17:\tlearn: 0.4574404\ttest: 0.4541869\tbest: 0.4541869 (17)\ttotal: 500ms\tremaining: 27.3s\n",
+      "18:\tlearn: 0.4528654\ttest: 0.4497378\tbest: 0.4497378 (18)\ttotal: 531ms\tremaining: 27.4s\n",
+      "19:\tlearn: 0.4482357\ttest: 0.4459038\tbest: 0.4459038 (19)\ttotal: 564ms\tremaining: 27.6s\n",
+      "20:\tlearn: 0.4447377\ttest: 0.4426946\tbest: 0.4426946 (20)\ttotal: 597ms\tremaining: 27.8s\n",
+      "21:\tlearn: 0.4414096\ttest: 0.4397617\tbest: 0.4397617 (21)\ttotal: 630ms\tremaining: 28s\n",
+      "22:\tlearn: 0.4383806\ttest: 0.4370010\tbest: 0.4370010 (22)\ttotal: 666ms\tremaining: 28.3s\n",
+      "23:\tlearn: 0.4357141\ttest: 0.4346098\tbest: 0.4346098 (23)\ttotal: 710ms\tremaining: 28.9s\n",
+      "24:\tlearn: 0.4340841\ttest: 0.4333834\tbest: 0.4333834 (24)\ttotal: 739ms\tremaining: 28.8s\n",
+      "25:\tlearn: 0.4316206\ttest: 0.4315814\tbest: 0.4315814 (25)\ttotal: 775ms\tremaining: 29s\n",
+      "26:\tlearn: 0.4291486\ttest: 0.4294672\tbest: 0.4294672 (26)\ttotal: 809ms\tremaining: 29.2s\n",
+      "27:\tlearn: 0.4272860\ttest: 0.4279721\tbest: 0.4279721 (27)\ttotal: 845ms\tremaining: 29.3s\n",
+      "28:\tlearn: 0.4255243\ttest: 0.4265458\tbest: 0.4265458 (28)\ttotal: 879ms\tremaining: 29.4s\n",
+      "29:\tlearn: 0.4237533\ttest: 0.4252464\tbest: 0.4252464 (29)\ttotal: 919ms\tremaining: 29.7s\n",
+      "30:\tlearn: 0.4220748\ttest: 0.4242429\tbest: 0.4242429 (30)\ttotal: 954ms\tremaining: 29.8s\n",
+      "31:\tlearn: 0.4208790\ttest: 0.4230531\tbest: 0.4230531 (31)\ttotal: 989ms\tremaining: 29.9s\n",
+      "32:\tlearn: 0.4196280\ttest: 0.4219187\tbest: 0.4219187 (32)\ttotal: 1.02s\tremaining: 30s\n",
+      "33:\tlearn: 0.4188299\ttest: 0.4213500\tbest: 0.4213500 (33)\ttotal: 1.06s\tremaining: 30.1s\n",
+      "34:\tlearn: 0.4175882\ttest: 0.4202557\tbest: 0.4202557 (34)\ttotal: 1.09s\tremaining: 30s\n",
+      "35:\tlearn: 0.4163712\ttest: 0.4194352\tbest: 0.4194352 (35)\ttotal: 1.12s\tremaining: 30s\n",
+      "36:\tlearn: 0.4152490\ttest: 0.4189529\tbest: 0.4189529 (36)\ttotal: 1.15s\tremaining: 30s\n",
+      "37:\tlearn: 0.4143886\ttest: 0.4184102\tbest: 0.4184102 (37)\ttotal: 1.19s\tremaining: 30.1s\n",
+      "38:\tlearn: 0.4134848\ttest: 0.4177953\tbest: 0.4177953 (38)\ttotal: 1.23s\tremaining: 30.2s\n",
+      "39:\tlearn: 0.4123643\ttest: 0.4172443\tbest: 0.4172443 (39)\ttotal: 1.26s\tremaining: 30.2s\n",
+      "40:\tlearn: 0.4112168\ttest: 0.4163683\tbest: 0.4163683 (40)\ttotal: 1.29s\tremaining: 30.2s\n",
+      "41:\tlearn: 0.4105189\ttest: 0.4158805\tbest: 0.4158805 (41)\ttotal: 1.32s\tremaining: 30.2s\n",
+      "42:\tlearn: 0.4093483\ttest: 0.4151292\tbest: 0.4151292 (42)\ttotal: 1.35s\tremaining: 30.2s\n",
+      "43:\tlearn: 0.4084758\ttest: 0.4149052\tbest: 0.4149052 (43)\ttotal: 1.39s\tremaining: 30.1s\n",
+      "44:\tlearn: 0.4078956\ttest: 0.4143354\tbest: 0.4143354 (44)\ttotal: 1.42s\tremaining: 30.1s\n",
+      "45:\tlearn: 0.4073410\ttest: 0.4139428\tbest: 0.4139428 (45)\ttotal: 1.45s\tremaining: 30s\n",
+      "46:\tlearn: 0.4065905\ttest: 0.4139550\tbest: 0.4139428 (45)\ttotal: 1.48s\tremaining: 30s\n",
+      "47:\tlearn: 0.4060895\ttest: 0.4136256\tbest: 0.4136256 (47)\ttotal: 1.51s\tremaining: 30s\n",
+      "48:\tlearn: 0.4059262\ttest: 0.4135441\tbest: 0.4135441 (48)\ttotal: 1.53s\tremaining: 29.8s\n",
+      "49:\tlearn: 0.4052365\ttest: 0.4129055\tbest: 0.4129055 (49)\ttotal: 1.57s\tremaining: 29.8s\n",
+      "50:\tlearn: 0.4045429\ttest: 0.4128889\tbest: 0.4128889 (50)\ttotal: 1.6s\tremaining: 29.8s\n",
+      "51:\tlearn: 0.4040049\ttest: 0.4129957\tbest: 0.4128889 (50)\ttotal: 1.65s\tremaining: 30.1s\n",
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+      "998:\tlearn: 0.2573330\ttest: 0.4326792\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 41.5ms\n",
+      "999:\tlearn: 0.2571166\ttest: 0.4327456\tbest: 0.4098372 (105)\ttotal: 41.5s\tremaining: 0us\n",
+      "\n",
+      "bestTest = 0.4098372131\n",
+      "bestIteration = 105\n",
+      "\n",
+      "Shrink model to first 106 iterations.\n",
+      "CatBoost model parameters:\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from catboost import CatBoostClassifier\n",
+    "\n",
+    "cat_features = [col for col in X_train.select_dtypes(include=[\"object\",\"int\"]) if col != \"tenure\"]\n",
+    "\n",
+    "clf = CatBoostClassifier()\n",
+    "\n",
+    "\n",
+    "clf.fit(X_train, y_train, \n",
+    "        cat_features=cat_features, \n",
+    "        eval_set=(X_val, y_val), \n",
+    "        verbose=True\n",
+    ")\n",
+    "\n",
+    "print('CatBoost model parameters:')\n",
+    "print(clf.get_params())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv-app-ai-ds",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Supervised-Unsupervised/customer_segmentation.ipynb b/notebooks/Supervised-Unsupervised/customer_segmentation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..604351b4baf3cb93ebcf4e718f332a57addc73b7
--- /dev/null
+++ b/notebooks/Supervised-Unsupervised/customer_segmentation.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customer segmentation for targeted marketing campaign\n",
+    "\n",
+    "https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis\n",
+    "\n",
+    "**People**\n",
+    "- ID: Customer's unique identifier\n",
+    "- Year_Birth: Customer's birth year\n",
+    "- Education: Customer's education level\n",
+    "- Marital_Status: Customer's marital status\n",
+    "- Income: Customer's yearly household income\n",
+    "- Kidhome: Number of children in customer's household\n",
+    "- Teenhome: Number of teenagers in customer's household\n",
+    "- Dt_Customer: Date of customer's enrollment with the company\n",
+    "- Recency: Number of days since customer's last purchase\n",
+    "- Complain: 1 if the customer complained in the last 2 years, 0 otherwise\n",
+    "\n",
+    "**Products**\n",
+    "- MntWines: Amount spent on wine in last 2 years\n",
+    "- MntFruits: Amount spent on fruits in last 2 years\n",
+    "- MntMeatProducts: Amount spent on meat in last 2 years\n",
+    "- MntFishProducts: Amount spent on fish in last 2 years\n",
+    "- MntSweetProducts: Amount spent on sweets in last 2 years\n",
+    "- MntGoldProds: Amount spent on gold in last 2 years\n",
+    "\n",
+    "**Promotion**\n",
+    "- NumDealsPurchases: Number of purchases made with a discount\n",
+    "- AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise\n",
+    "- AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise\n",
+    "- AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise\n",
+    "- AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise\n",
+    "- AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise\n",
+    "- Response: 1 if customer accepted the offer in the last campaign, 0 otherwise\n",
+    "\n",
+    "**Place**\n",
+    "- NumWebPurchases: Number of purchases made through the company’s website\n",
+    "- NumCatalogPurchases: Number of purchases made using a catalogue\n",
+    "- NumStorePurchases: Number of purchases made directly in stores\n",
+    "- NumWebVisitsMonth: Number of visits to company’s website in the last month"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load dataset\n",
+    "path_data_marketing = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\\marketing_campaign.csv\"\n",
+    "marketing_data = pd.read_csv(path_data_marketing, sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1364,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete columns\n",
+    "marketing_data.drop(columns=['ID','MntGoldProds','Response','Complain','AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1','AcceptedCmp2',\n",
+    "                             'Z_CostContact', 'Z_Revenue'], inplace=True)\n",
+    "\n",
+    "#marketing_data = marketing_data.loc[marketing_data[\"Marital_Status\"].isin([\"Single\",\"Married\",\"Divorced\"])]\n",
+    "marketing_data.drop(columns=[\"Marital_Status\"], inplace=True)\n",
+    "\n",
+    "# marketing_data = marketing_data.loc[marketing_data[\"Education\"].isin([\"2n Cycle\",\"Graduation\",\"Master\",\"PhD\"])]\n",
+    "marketing_data.drop(columns=[\"Education\"],inplace=True)\n",
+    "\n",
+    "marketing_data = marketing_data[marketing_data[\"Income\"]>5000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1365,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change column names\n",
+    "new_columns = [col.replace(\"Mnt\",\"\").replace(\"Num\",\"\") for col in list(marketing_data.columns)]\n",
+    "new_columns = [col + \"Products\" if col in [\"Wines\",\"Fruits\"] else col for col in new_columns]\n",
+    "marketing_data.columns = new_columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1366,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Proportion of a customer's income spent on wines, fruits, ...\n",
+    "products_col = [\"WinesProducts\",\"FruitsProducts\", \"MeatProducts\",\"FishProducts\",\"SweetProducts\"]\n",
+    "total_amount_spent = marketing_data[products_col].sum(axis=1)\n",
+    "\n",
+    "for col in products_col:\n",
+    "    marketing_data[col] = (100*marketing_data[col] / total_amount_spent).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1367,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Proportion of web, catalog and store purchases (based on total number of purchases)\n",
+    "purchases_col = [\"WebPurchases\", \"CatalogPurchases\", \"StorePurchases\"]\n",
+    "total_purchases = marketing_data[purchases_col].sum(axis=1)\n",
+    "\n",
+    "for col in purchases_col:\n",
+    "    marketing_data[col] = (100*marketing_data[col] / total_purchases).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1368,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, date\n",
+    "\n",
+    "def get_number_days(input_date):\n",
+    "    date1 = datetime.strptime(input_date, '%d/%m/%Y').date()\n",
+    "    date2 = date(2022, 2, 13)\n",
+    "    return (date2 - date1).days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute a customer's age, based on year of birth\n",
+    "marketing_data.insert(0, \"Age\", marketing_data[\"Year_Birth\"].apply(lambda x: 2023-x))\n",
+    "\n",
+    "# Compute the number of days a customer has been subscribed \n",
+    "marketing_data.insert(1, \"Days_subscription\", marketing_data[\"Dt_Customer\"].apply(get_number_days))\n",
+    "\n",
+    "# Compute total number of kids (kids + teens)\n",
+    "marketing_data[\"Kids\"] = marketing_data[\"Kidhome\"] + marketing_data[\"Teenhome\"]\n",
+    "marketing_data.drop(columns=[\"Kidhome\",\"Teenhome\"], inplace=True)\n",
+    "\n",
+    "marketing_data.drop(columns=[\"Year_Birth\", \"Dt_Customer\"], inplace=True)\n",
+    "marketing_data.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1370,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_cleandata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\"\n",
+    "marketing_data.to_pickle(os.path.join(path_cleandata,\"clean_marketing.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1371,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Days_subscription</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Recency</th>\n",
+       "      <th>WinesProducts</th>\n",
+       "      <th>FruitsProducts</th>\n",
+       "      <th>MeatProducts</th>\n",
+       "      <th>FishProducts</th>\n",
+       "      <th>SweetProducts</th>\n",
+       "      <th>DealsPurchases</th>\n",
+       "      <th>WebPurchases</th>\n",
+       "      <th>CatalogPurchases</th>\n",
+       "      <th>StorePurchases</th>\n",
+       "      <th>WebVisitsMonth</th>\n",
+       "      <th>Kids</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>66</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>58138.0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>35.7</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36.4</td>\n",
+       "      <td>45.5</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>69</td>\n",
+       "      <td>2899</td>\n",
+       "      <td>46344.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>58</td>\n",
+       "      <td>3098</td>\n",
+       "      <td>71613.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39</td>\n",
+       "      <td>2925</td>\n",
+       "      <td>26646.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>22.9</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>41.7</td>\n",
+       "      <td>20.8</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42</td>\n",
+       "      <td>2947</td>\n",
+       "      <td>58293.0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>42.5</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>35.7</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>42.9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2235</th>\n",
+       "      <td>56</td>\n",
+       "      <td>3167</td>\n",
+       "      <td>61223.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>64.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>56.2</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2236</th>\n",
+       "      <td>77</td>\n",
+       "      <td>2805</td>\n",
+       "      <td>64014.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>93.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>53.3</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2237</th>\n",
+       "      <td>42</td>\n",
+       "      <td>2941</td>\n",
+       "      <td>56981.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>74.6</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>72.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2238</th>\n",
+       "      <td>67</td>\n",
+       "      <td>2942</td>\n",
+       "      <td>69245.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>54.7</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>27.4</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>23.8</td>\n",
+       "      <td>47.6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2239</th>\n",
+       "      <td>69</td>\n",
+       "      <td>3408</td>\n",
+       "      <td>52869.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>55.6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.4</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>37.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2208 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Age  Days_subscription   Income  Recency  WinesProducts  FruitsProducts  \\\n",
+       "0      66               3449  58138.0       58           41.5             5.8   \n",
+       "1      69               2899  46344.0       38           52.4             4.8   \n",
+       "2      58               3098  71613.0       26           58.0             6.7   \n",
+       "3      39               2925  26646.0       26           22.9             8.3   \n",
+       "4      42               2947  58293.0       94           42.5            10.6   \n",
+       "...   ...                ...      ...      ...            ...             ...   \n",
+       "2235   56               3167  61223.0       46           64.8             3.9   \n",
+       "2236   77               2805  64014.0       56           93.1             0.0   \n",
+       "2237   42               2941  56981.0       91           74.6             3.9   \n",
+       "2238   67               2942  69245.0        8           54.7             3.8   \n",
+       "2239   69               3408  52869.0       40           55.6             2.0   \n",
+       "\n",
+       "      MeatProducts  FishProducts  SweetProducts  DealsPurchases  WebPurchases  \\\n",
+       "0             35.7          11.2            5.8               3          36.4   \n",
+       "1             28.6           9.5            4.8               2          25.0   \n",
+       "2             17.3          15.1            2.9               1          40.0   \n",
+       "3             41.7          20.8            6.2               2          33.3   \n",
+       "4             29.0          11.3            6.6               5          35.7   \n",
+       "...            ...           ...            ...             ...           ...   \n",
+       "2235          16.6           3.8           10.8               2          56.2   \n",
+       "2236           6.9           0.0            0.0               7          53.3   \n",
+       "2237          17.8           2.6            1.0               1          11.1   \n",
+       "2238          27.4          10.2            3.8               2          28.6   \n",
+       "2239          40.4           1.3            0.7               3          37.5   \n",
+       "\n",
+       "      CatalogPurchases  StorePurchases  WebVisitsMonth  Kids  \n",
+       "0                 45.5            18.2               7     0  \n",
+       "1                 25.0            50.0               5     2  \n",
+       "2                 10.0            50.0               4     0  \n",
+       "3                  0.0            66.7               6     1  \n",
+       "4                 21.4            42.9               5     1  \n",
+       "...                ...             ...             ...   ...  \n",
+       "2235              18.8            25.0               5     1  \n",
+       "2236              13.3            33.3               7     3  \n",
+       "2237              16.7            72.2               6     0  \n",
+       "2238              23.8            47.6               3     1  \n",
+       "2239              12.5            50.0               7     2  \n",
+       "\n",
+       "[2208 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 1371,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_pickle(os.path.join(path_cleandata,\"clean_marketing.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1372,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
+    "\n",
+    "num_columns = marketing_data.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
+    "\n",
+    "# Build data processing pipeline\n",
+    "ct = ColumnTransformer(\n",
+    "    [(\"numerical\", RobustScaler(), num_columns)])\n",
+    "\n",
+    "X = ct.fit_transform(marketing_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1373,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
+    "df_clean = pd.DataFrame(X, columns=columns_transform)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1374,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "\n",
+    "def clustering_model(X, list_nb_clusters):\n",
+    "    dict_labels = dict()\n",
+    "    list_scores = []\n",
+    "\n",
+    "    for n in list_nb_clusters:\n",
+    "        kmeans = KMeans(n_clusters=n, n_init=10)\n",
+    "        labels = kmeans.fit_predict(X)\n",
+    "        score = silhouette_score(X, labels)\n",
+    "        dict_labels[f\"{n} clusters\"] = labels\n",
+    "        list_scores.append(score)\n",
+    "\n",
+    "    return list_scores, dict_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1375,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_nb_clusters = np.arange(2,7)\n",
+    "scores_kmeans, labels_kmeans = clustering_model(X, list_nb_clusters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1376,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "marketing_data_results = pd.DataFrame({\"nb_clusters\":[str(i) for i in np.arange(2,7)], \"scores\":scores_kmeans})\n",
+    "\n",
+    "sns.lineplot(data=marketing_data_results, x=\"nb_clusters\", y=\"scores\", marker=\"o\")\n",
+    "plt.xlabel(\"number of clusters\")\n",
+    "plt.ylabel(\"silhouette score\")\n",
+    "plt.title(\"Silhouette score of Kmeans\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1377,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "path_results = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\\results\"\n",
+    "\n",
+    "for nb_clusters in list_nb_clusters:\n",
+    "    labels_ = labels_kmeans[f\"{nb_clusters} clusters\"] # chosen labels\n",
+    "    marketing_data_labels = marketing_data.copy()\n",
+    "    marketing_data_labels[\"Group\"] = labels_\n",
+    "    marketing_data_labels[\"Group\"] = marketing_data_labels[\"Group\"].astype(int)\n",
+    "\n",
+    "    df_mean_results = marketing_data_labels.groupby(\"Group\")[num_columns].mean().reset_index()\n",
+    "    df_mean_results = df_mean_results.round(1).melt(id_vars=[\"Group\"])\n",
+    "    df_mean_results = pd.pivot_table(df_mean_results, values='value', index=['variable'], columns=[\"Group\"])\n",
+    "\n",
+    "    df_mean_results.to_pickle(os.path.join(path_results,f\"results_{nb_clusters}_clusters.pkl\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Supervised-Unsupervised/supply_chain.ipynb b/notebooks/Supervised-Unsupervised/supply_chain.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2793c0d555769feaf50da361f19ca1435b94897c
--- /dev/null
+++ b/notebooks/Supervised-Unsupervised/supply_chain.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\supply_chain_data.csv\"\n",
+    "supply_data = pd.read_csv(path_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/customer_review_polarity.ipynb b/notebooks/customer_review_polarity.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..530fad426683a0b8d2abfc32b46ab992840de9f2
--- /dev/null
+++ b/notebooks/customer_review_polarity.ipynb
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_sa = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\reviews_data.csv\"\n",
+    "data = pd.read_csv(os.path.join(path_sa,\"reviews_data.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 850 entries, 0 to 849\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   name         850 non-null    object \n",
+      " 1   location     850 non-null    object \n",
+      " 2   Date         850 non-null    object \n",
+      " 3   Rating       705 non-null    float64\n",
+      " 4   Review       850 non-null    object \n",
+      " 5   Image_Links  850 non-null    object \n",
+      "dtypes: float64(1), object(5)\n",
+      "memory usage: 40.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.loc[data[\"Review\"]!=\"No Review Text\"].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_out = data.loc[data[\"Image_Links\"]!=\"['No Images']\"].index\n",
+    "index_in = [i for i in list(data.index) if i not in list(index_out)]\n",
+    "add_data = data.iloc[index_in].sample(54)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews_data_clean = pd.concat([data.iloc[index_out].reset_index(drop=True),\n",
+    "                                add_data.reset_index(drop=True)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_location(x):\n",
+    "    state = x.split(\",\")[1].strip().upper()\n",
+    "    if state == \"CALIFORNIA\":\n",
+    "        state = \"CA\"\n",
+    "    if state == \"ALBERTA\":\n",
+    "        state = \"OTHER\"\n",
+    "\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews_data_clean[\"location\"] = reviews_data_clean[\"location\"].apply(clean_location)\n",
+    "reviews_data_clean[\"Date\"] = reviews_data_clean[\"Date\"].apply(lambda x: x.split(\"Reviewed\")[1].strip())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FINAL DATASET\n",
+    "reviews_data_final = reviews_data_clean.loc[reviews_data_clean[\"location\"].isin([\"CA\",\"TX\",\"PA\",\"OR\",\"MO\",\"MN\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "def format_date(date):\n",
+    "    if \"Jan.\" in date:\n",
+    "        date = date.replace(\"Jan.\",\"January\")\n",
+    "    if \"Feb.\" in date:\n",
+    "        date = date.replace(\"Feb.\", \"February\")\n",
+    "    if \"Aug.\" in date:\n",
+    "        date = date.replace(\"Aug.\", \"August\")\n",
+    "    if \"Sept.\" in date:\n",
+    "        date = date.replace(\"Sept.\", \"September\")\n",
+    "    if \"Oct.\" in date:\n",
+    "        date = date.replace(\"Oct.\", \"October\")\n",
+    "    if \"Nov.\" in date: \n",
+    "        date = date.replace(\"Nov.\", \"November\")\n",
+    "    if \"Dec.\" in date:\n",
+    "        date = date.replace(\"Dec.\", \"December\")\n",
+    "\n",
+    "    date = date.replace(\",\",\"\")\n",
+    "\n",
+    "    parsed_date = datetime.strptime(date, \"%B %d %Y\")\n",
+    "\n",
+    "    # Format the date as MM/DD/YYYY\n",
+    "    formatted_date = parsed_date.strftime(\"%m-%d-%Y\")\n",
+    "    \n",
+    "    return formatted_date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\1306421503.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final[\"Date\"] = pd.to_datetime(reviews_data_final[\"Date\"].apply(format_date))\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews_data_final[\"Date\"] = pd.to_datetime(reviews_data_final[\"Date\"].apply(format_date))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict_states = {\"CA\":\"California\",\n",
+    "               \"TX\":\"Texas\",\n",
+    "               \"PA\":\"Pennsylvania\",\n",
+    "               \"OR\":\"Oregon\",\n",
+    "               \"MO\":\"Missouri\",\n",
+    "               \"MN\":\"Minnesota\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\3901736406.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final[\"location\"] = reviews_data_final[\"location\"].map(dict_states)\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews_data_final[\"location\"] = reviews_data_final[\"location\"].map(dict_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\332411794.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final.drop(columns=[\"name\"],inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews_data_final.drop(columns=[\"name\"],inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re \n",
+    "\n",
+    "def clean_text(text):\n",
+    "    pattern_punct = r\"[^\\w\\s.',:/]\"\n",
+    "    pattern_date = r'\\b\\d{1,2}/\\d{1,2}/\\d{2,4}\\b'\n",
+    "\n",
+    "    text = text.lower()\n",
+    "    text = re.sub(pattern_date, '', text)\n",
+    "    text = re.sub(pattern_punct, '', text)\n",
+    "    text = text.replace(\"ggg\",\"g\")\n",
+    "    text = text.replace(\"  \",\" \")\n",
+    "\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_image_urls = [url.replace(\"['\",\"\").replace(\"']\",\"\").replace(\"',\",\" \").replace(\"'\",'') for url in reviews_data_final[\"Image_Links\"].to_list()]\n",
+    "clean_image_urls = [elem.split(\"  \") for elem in clean_image_urls]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images_1 = []\n",
+    "images_2 = []\n",
+    "\n",
+    "for elem in clean_image_urls:\n",
+    "\n",
+    "    if elem[0] != \"No Images\":\n",
+    "        images_1.append(elem[0])\n",
+    "    else: \n",
+    "        images_1.append(np.nan)\n",
+    "\n",
+    "    if len(elem) == 2:\n",
+    "        images_2.append(elem[1])\n",
+    "    else:\n",
+    "        images_2.append(np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\464558646.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final[\"Image 1\"] = images_1\n",
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\464558646.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final[\"Image 2\"] = images_2\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews_data_final[\"Image 1\"] = images_1\n",
+    "reviews_data_final[\"Image 2\"] = images_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\1924389021.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  reviews_data_final.rename({\"location\":\"State\"}, axis=1, inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews_data_final.rename({\"location\":\"State\"}, axis=1, inplace=True)\n",
+    "reviews_data_final.drop(columns=[\"Image_Links\"])\n",
+    "reviews_data_final = reviews_data_final[[\"Date\",\"State\",\"Review\",\"Rating\",\"Image 1\",\"Image 2\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews_data_final[\"Year\"] = reviews_data_final[\"Date\"].dt.year\n",
+    "reviews_data_final.insert(0, \"ID\", [f\"{i}\" for i in np.arange(1, len(reviews_data_final)+1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews_data_final.to_pickle(os.path.join(path_sa,\"reviews_raw.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews_data_final_clean = reviews_data_final.copy()\n",
+    "reviews_data_final_clean[\"Review\"] = reviews_data_final_clean[\"Review\"].apply(clean_text)\n",
+    "# reviews_data_final.to_pickle(os.path.join(path_sa,\"reviews_clean.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pysentimiento import create_analyzer\n",
+    "\n",
+    "list_reviews = reviews_data_final_clean[\"Review\"].to_list()\n",
+    "sentiment_analyzer = create_analyzer(task=\"sentiment\", lang=\"en\")\n",
+    "predictions = []\n",
+    "positive = []\n",
+    "negative = []\n",
+    "neutral = []\n",
+    "\n",
+    "for review in list_reviews:\n",
+    "    #if review.split(\" \")\n",
+    "    q = sentiment_analyzer.predict(review)\n",
+    "\n",
+    "    predictions.append(q.output)\n",
+    "    positive.append(q.probas[\"POS\"])\n",
+    "    negative.append(q.probas[\"NEG\"])\n",
+    "    neutral.append(q.probas[\"NEU\"])\n",
+    "\n",
+    "# Results\n",
+    "df_results = reviews_data_final_clean.copy()\n",
+    "df_results[\"Result\"] = predictions\n",
+    "df_results[\"Result\"] = df_results[\"Result\"].map({\"NEU\":\"Neutral\", \"NEG\":\"Negative\", \"POS\":\"Positive\"})\n",
+    "df_results[\"Negative\"] = np.round(np.array(negative)*100)\n",
+    "df_results[\"Neutral\"] = np.round(np.array(neutral)*100)\n",
+    "df_results[\"Positive\"] = np.round(np.array(positive)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results.to_pickle(os.path.join(path_sa,\"reviews_results.pkl\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/energy_consumption.ipynb b/notebooks/energy_consumption.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..eee2751034abaa389176ca92d73d32ebb265750e
--- /dev/null
+++ b/notebooks/energy_consumption.ipynb
@@ -0,0 +1,4563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Household energy consumption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from prophet import Prophet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\data-hec-AI-DS\\household_power_consumption.txt\"\n",
+    "data = pd.read_csv(path_data, low_memory=False, sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2075259 entries, 0 to 2075258\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column                 Dtype  \n",
+      "---  ------                 -----  \n",
+      " 0   Date                   object \n",
+      " 1   Time                   object \n",
+      " 2   Global_active_power    object \n",
+      " 3   Global_reactive_power  object \n",
+      " 4   Voltage                object \n",
+      " 5   Global_intensity       object \n",
+      " 6   Sub_metering_1         object \n",
+      " 7   Sub_metering_2         object \n",
+      " 8   Sub_metering_3         float64\n",
+      "dtypes: float64(1), object(8)\n",
+      "memory usage: 142.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in data.columns[2:]:\n",
+    "    data[col] = data[col].apply(lambda x: np.nan if x==\"?\" else x)\n",
+    "    data[col] = data[col].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Average energy consumption to daily measurements\n",
+    "data_clean = data.drop(columns=[\"Time\"]).groupby(\"Date\").mean().reset_index()\n",
+    "data_clean[\"Date\"] = pd.to_datetime(data_clean[\"Date\"], format=\"%d/%m/%Y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Global_active_power</th>\n",
+       "      <th>Global_reactive_power</th>\n",
+       "      <th>Voltage</th>\n",
+       "      <th>Global_intensity</th>\n",
+       "      <th>Sub_metering_1</th>\n",
+       "      <th>Sub_metering_2</th>\n",
+       "      <th>Sub_metering_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1437</th>\n",
+       "      <td>2010-08-09</td>\n",
+       "      <td>0.371572</td>\n",
+       "      <td>0.119092</td>\n",
+       "      <td>241.069264</td>\n",
+       "      <td>1.638472</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.357639</td>\n",
+       "      <td>2.806944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1438</th>\n",
+       "      <td>2007-09-09</td>\n",
+       "      <td>1.166321</td>\n",
+       "      <td>0.145244</td>\n",
+       "      <td>238.922146</td>\n",
+       "      <td>5.054028</td>\n",
+       "      <td>1.516667</td>\n",
+       "      <td>3.081944</td>\n",
+       "      <td>6.411806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1439</th>\n",
+       "      <td>2008-09-09</td>\n",
+       "      <td>0.805315</td>\n",
+       "      <td>0.125465</td>\n",
+       "      <td>239.781667</td>\n",
+       "      <td>3.464444</td>\n",
+       "      <td>0.163889</td>\n",
+       "      <td>0.314583</td>\n",
+       "      <td>5.366667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1440</th>\n",
+       "      <td>2009-09-09</td>\n",
+       "      <td>1.056489</td>\n",
+       "      <td>0.155258</td>\n",
+       "      <td>241.308507</td>\n",
+       "      <td>4.491250</td>\n",
+       "      <td>0.669444</td>\n",
+       "      <td>2.661111</td>\n",
+       "      <td>7.410417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1441</th>\n",
+       "      <td>2010-09-09</td>\n",
+       "      <td>0.860218</td>\n",
+       "      <td>0.116635</td>\n",
+       "      <td>240.603215</td>\n",
+       "      <td>3.701667</td>\n",
+       "      <td>1.259028</td>\n",
+       "      <td>1.477778</td>\n",
+       "      <td>4.872222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date  Global_active_power  Global_reactive_power     Voltage  \\\n",
+       "1437 2010-08-09             0.371572               0.119092  241.069264   \n",
+       "1438 2007-09-09             1.166321               0.145244  238.922146   \n",
+       "1439 2008-09-09             0.805315               0.125465  239.781667   \n",
+       "1440 2009-09-09             1.056489               0.155258  241.308507   \n",
+       "1441 2010-09-09             0.860218               0.116635  240.603215   \n",
+       "\n",
+       "      Global_intensity  Sub_metering_1  Sub_metering_2  Sub_metering_3  \n",
+       "1437          1.638472        0.000000        0.357639        2.806944  \n",
+       "1438          5.054028        1.516667        3.081944        6.411806  \n",
+       "1439          3.464444        0.163889        0.314583        5.366667  \n",
+       "1440          4.491250        0.669444        2.661111        7.410417  \n",
+       "1441          3.701667        1.259028        1.477778        4.872222  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_clean.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in data_clean.columns[1:]:\n",
+    "    data_clean[col] = data_clean[col].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_clean[\"Date\"] = pd.to_datetime(data_clean[\"Date\"])\n",
+    "data_clean.sort_values(by=[\"Date\"], inplace=True)\n",
+    "data_clean.drop(columns=[\"Global_reactive_power\",\"Voltage\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Global_active_power</th>\n",
+       "      <th>Global_intensity</th>\n",
+       "      <th>Sub_metering_1</th>\n",
+       "      <th>Sub_metering_2</th>\n",
+       "      <th>Sub_metering_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>2006-12-16</td>\n",
+       "      <td>3.05</td>\n",
+       "      <td>13.08</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.38</td>\n",
+       "      <td>12.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2006-12-17</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>2.91</td>\n",
+       "      <td>9.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>2006-12-18</td>\n",
+       "      <td>1.53</td>\n",
+       "      <td>6.42</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>9.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>2006-12-19</td>\n",
+       "      <td>1.16</td>\n",
+       "      <td>4.93</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>5.28</td>\n",
+       "      <td>4.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>2006-12-20</td>\n",
+       "      <td>1.55</td>\n",
+       "      <td>6.47</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>9.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>2010-11-22</td>\n",
+       "      <td>1.42</td>\n",
+       "      <td>6.01</td>\n",
+       "      <td>3.37</td>\n",
+       "      <td>1.47</td>\n",
+       "      <td>7.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>2010-11-23</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>4.67</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>5.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>2010-11-24</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>1.98</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>819</th>\n",
+       "      <td>2010-11-25</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>4.17</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>867</th>\n",
+       "      <td>2010-11-26</td>\n",
+       "      <td>1.18</td>\n",
+       "      <td>4.96</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>7.91</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1442 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Date  Global_active_power  Global_intensity  Sub_metering_1  \\\n",
+       "341 2006-12-16                 3.05             13.08            0.00   \n",
+       "389 2006-12-17                 2.35             10.00            1.41   \n",
+       "437 2006-12-18                 1.53              6.42            0.74   \n",
+       "485 2006-12-19                 1.16              4.93            0.58   \n",
+       "580 2006-12-20                 1.55              6.47            0.00   \n",
+       "..         ...                  ...               ...             ...   \n",
+       "675 2010-11-22                 1.42              6.01            3.37   \n",
+       "723 2010-11-23                 1.10              4.67            1.30   \n",
+       "771 2010-11-24                 1.25              5.25            0.76   \n",
+       "819 2010-11-25                 0.99              4.17            0.75   \n",
+       "867 2010-11-26                 1.18              4.96            0.86   \n",
+       "\n",
+       "     Sub_metering_2  Sub_metering_3  \n",
+       "341            1.38           12.44  \n",
+       "389            2.91            9.26  \n",
+       "437            1.82            9.73  \n",
+       "485            5.28            4.30  \n",
+       "580            1.84            9.77  \n",
+       "..              ...             ...  \n",
+       "675            1.47            7.04  \n",
+       "723            0.32            5.29  \n",
+       "771            1.98            8.49  \n",
+       "819            0.30            3.52  \n",
+       "867            0.30            7.91  \n",
+       "\n",
+       "[1442 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_clean_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\household\\household_power_consumption_clean.pkl\"\n",
+    "data_clean.to_pickle(path_clean_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_clean.rename({\"Date\":\"ds\", \"Global_active_power\":\"y\"},axis=1, inplace=True)\n",
+    "data_clean.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.lineplot(data=data_clean, x=\"ds\", y=\"y\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.lineplot(data=data_clean, x=\"ds\", y=\"Sub_metering_3\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_date = '2010-01-01'\n",
+    "\n",
+    "train = data_clean[data_clean[\"ds\"] <= cutoff_date]\n",
+    "test = data_clean[data_clean[\"ds\"] > cutoff_date]\n",
+    "\n",
+    "m = Prophet(daily_seasonality=False)\n",
+    "# for col in [\"Global_intensity\", \"Sub_metering_1\", \"Sub_metering_2\", \"Sub_metering_3\"]:\n",
+    "#     m.add_regressor(col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_plot = train.copy()\n",
+    "train_plot[\"split\"] = [\"train\"]*len(train_plot)\n",
+    "\n",
+    "test_plot = test.copy()\n",
+    "test_plot[\"split\"] = [\"test\"]*len(test_plot)\n",
+    "\n",
+    "data_clean_plot = pd.concat([train_plot, test_plot])\n",
+    "\n",
+    "plt.figure(figsize=(10,4))\n",
+    "sns.lineplot(data=data_clean_plot, x=\"ds\", y=\"y\", hue=\"split\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "12:14:32 - cmdstanpy - INFO - Chain [1] start processing\n",
+      "12:14:32 - cmdstanpy - INFO - Chain [1] done processing\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<prophet.forecaster.Prophet at 0x17a91e247c0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.fit(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forecast = m.predict(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\HEC_EMBA\\app_hec_emba\\venv\\lib\\site-packages\\prophet\\plot.py:72: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
+      "  fcst_t = fcst['ds'].dt.to_pydatetime()\n",
+      "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\HEC_EMBA\\app_hec_emba\\venv\\lib\\site-packages\\prophet\\plot.py:73: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
+      "  ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.',\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.plot(forecast)\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Global Average Power')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results = test_plot.merge(forecast, on=\"ds\", how=\"left\")[[\"ds\",\"y\",\"yhat\"]]\n",
+    "df_results = df_results.melt(id_vars=\"ds\")\n",
+    "df_results[\"variable\"] = df_results[\"variable\"].map({\"y\":\"true values\", \"yhat\":\"predicted values\"})\n",
+    "df_results.columns = [\"Date\", \"Variable\", \"Global Active Power\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "Variable=true values<br>Date=%{x}<br>Global Active Power=%{y}<extra></extra>",
+         "legendgroup": "true values",
+         "line": {
+          "color": "black",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines",
+         "name": "true values",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "scatter",
+         "x": [
+          "2010-01-02T00:00:00",
+          "2010-01-03T00:00:00",
+          "2010-01-04T00:00:00",
+          "2010-01-05T00:00:00",
+          "2010-01-06T00:00:00",
+          "2010-01-07T00:00:00",
+          "2010-01-08T00:00:00",
+          "2010-01-09T00:00:00",
+          "2010-01-10T00:00:00",
+          "2010-01-11T00:00:00",
+          "2010-01-12T00:00:00",
+          "2010-01-14T00:00:00",
+          "2010-01-15T00:00:00",
+          "2010-01-16T00:00:00",
+          "2010-01-17T00:00:00",
+          "2010-01-18T00:00:00",
+          "2010-01-19T00:00:00",
+          "2010-01-20T00:00:00",
+          "2010-01-21T00:00:00",
+          "2010-01-22T00:00:00",
+          "2010-01-23T00:00:00",
+          "2010-01-24T00:00:00",
+          "2010-01-25T00:00:00",
+          "2010-01-26T00:00:00",
+          "2010-01-27T00:00:00",
+          "2010-01-28T00:00:00",
+          "2010-01-29T00:00:00",
+          "2010-01-30T00:00:00",
+          "2010-01-31T00:00:00",
+          "2010-02-01T00:00:00",
+          "2010-02-02T00:00:00",
+          "2010-02-03T00:00:00",
+          "2010-02-04T00:00:00",
+          "2010-02-05T00:00:00",
+          "2010-02-06T00:00:00",
+          "2010-02-07T00:00:00",
+          "2010-02-08T00:00:00",
+          "2010-02-09T00:00:00",
+          "2010-02-10T00:00:00",
+          "2010-02-11T00:00:00",
+          "2010-02-12T00:00:00",
+          "2010-02-13T00:00:00",
+          "2010-02-14T00:00:00",
+          "2010-02-15T00:00:00",
+          "2010-02-16T00:00:00",
+          "2010-02-17T00:00:00",
+          "2010-02-18T00:00:00",
+          "2010-02-19T00:00:00",
+          "2010-02-20T00:00:00",
+          "2010-02-21T00:00:00",
+          "2010-02-22T00:00:00",
+          "2010-02-23T00:00:00",
+          "2010-02-24T00:00:00",
+          "2010-02-25T00:00:00",
+          "2010-02-26T00:00:00",
+          "2010-02-27T00:00:00",
+          "2010-02-28T00:00:00",
+          "2010-03-01T00:00:00",
+          "2010-03-02T00:00:00",
+          "2010-03-03T00:00:00",
+          "2010-03-04T00:00:00",
+          "2010-03-05T00:00:00",
+          "2010-03-06T00:00:00",
+          "2010-03-07T00:00:00",
+          "2010-03-08T00:00:00",
+          "2010-03-09T00:00:00",
+          "2010-03-10T00:00:00",
+          "2010-03-11T00:00:00",
+          "2010-03-12T00:00:00",
+          "2010-03-13T00:00:00",
+          "2010-03-14T00:00:00",
+          "2010-03-15T00:00:00",
+          "2010-03-16T00:00:00",
+          "2010-03-17T00:00:00",
+          "2010-03-18T00:00:00",
+          "2010-03-19T00:00:00",
+          "2010-03-20T00:00:00",
+          "2010-03-21T00:00:00",
+          "2010-03-22T00:00:00",
+          "2010-03-23T00:00:00",
+          "2010-03-24T00:00:00",
+          "2010-03-25T00:00:00",
+          "2010-03-26T00:00:00",
+          "2010-03-27T00:00:00",
+          "2010-03-28T00:00:00",
+          "2010-03-29T00:00:00",
+          "2010-03-30T00:00:00",
+          "2010-03-31T00:00:00",
+          "2010-04-01T00:00:00",
+          "2010-04-02T00:00:00",
+          "2010-04-03T00:00:00",
+          "2010-04-04T00:00:00",
+          "2010-04-05T00:00:00",
+          "2010-04-06T00:00:00",
+          "2010-04-07T00:00:00",
+          "2010-04-08T00:00:00",
+          "2010-04-09T00:00:00",
+          "2010-04-10T00:00:00",
+          "2010-04-11T00:00:00",
+          "2010-04-12T00:00:00",
+          "2010-04-13T00:00:00",
+          "2010-04-14T00:00:00",
+          "2010-04-15T00:00:00",
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+           "caxis": {
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+          "title": {
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+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "True vs Predicted Values"
+        },
+        "width": 1200,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Date"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Global Active Power"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "# Create the first line plot\n",
+    "fig = px.line(df_results, x=\"Date\", y=\"Global Active Power\", color=\"Variable\", \n",
+    "              color_discrete_sequence=[\"black\", \"red\"], line_dash = 'Variable')\n",
+    "\n",
+    "# fig.update_traces(line=dict(dash=['solid', 'dash']), \n",
+    "#                   name=['Line 1', 'Line 2'])\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title='True vs Predicted Values',\n",
+    "    xaxis_title='Date',\n",
+    "    yaxis_title='Global Active Power',\n",
+    "    #legend=dict(title='Lines'),\n",
+    "    width=1200,  # Set the width of the figure\n",
+    "    height=600  # Set the height of the figure\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()\n",
+    "\n",
+    "# df = px.data.gapminder().query(\"continent == 'Oceania'\")\n",
+    "# fig = px.line(df, x='year', y='lifeExp', color='country', markers=True)\n",
+    "# fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2632010945496168"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import root_mean_squared_error\n",
+    "y_true = test_plot[\"y\"]\n",
+    "y_pred = forecast[\"yhat\"]\n",
+    "\n",
+    "root_mean_squared_error(y_true, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "sns.lineplot(data=test_plot, x=\"ds\", y=\"y\") # true (in blue)\n",
+    "sns.lineplot(data=forecast, x=\"ds\", y=\"yhat\", color=\"black\", linestyle='--') # predicted (in black)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=data_clean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "days_week = dict(zip(np.arange(1,8),[\"Monday\", \"Tuedsay\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]))\n",
+    "forecast_weekly = forecast.copy()\n",
+    "forecast_weekly[\"dayweek\"] = forecast_weekly[\"ds\"].apply(lambda x: x.isoweekday()).map(days_week)\n",
+    "\n",
+    "plt.figure(figsize=(10,4))\n",
+    "df_weekly = forecast_weekly[[\"weekly\",\"dayweek\"]]\n",
+    "sns.lineplot(data=df_weekly, x=\"dayweek\", y=\"weekly\")\n",
+    "plt.xlabel(\"Day of week\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\HEC_EMBA\\app_hec_emba\\venv\\lib\\site-packages\\prophet\\plot.py:228: FutureWarning:\n",
+      "\n",
+      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
+      "\n",
+      "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\HEC_EMBA\\app_hec_emba\\venv\\lib\\site-packages\\prophet\\plot.py:351: FutureWarning:\n",
+      "\n",
+      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
+      "\n",
+      "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\HEC_EMBA\\app_hec_emba\\venv\\lib\\site-packages\\prophet\\plot.py:354: FutureWarning:\n",
+      "\n",
+      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 3 Axes>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m.plot_components(forecast)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forecast_year = forecast[[\"ds\",\"yearly\"]].copy()\n",
+    "forecast_year[\"ds_year\"] = forecast_year[\"ds\"].apply(lambda x: x.strftime(\"%B %d\"))\n",
+    "forecast_year[\"ds\"] = forecast_year[\"ds\"].apply(lambda x: x.strftime(\"%m-%d\"))\n",
+    "forecast_year.sort_values(by=[\"ds\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "fillpattern": {
+          "shape": ""
+         },
+         "hovertemplate": "ds_year=%{x}<br>yearly=%{y}<extra></extra>",
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "stackgroup": "1",
+         "type": "scatter",
+         "x": [
+          "January 02",
+          "January 03",
+          "January 04",
+          "January 05",
+          "January 06",
+          "January 07",
+          "January 08",
+          "January 09",
+          "January 10",
+          "January 11",
+          "January 12",
+          "January 14",
+          "January 15",
+          "January 16",
+          "January 17",
+          "January 18",
+          "January 19",
+          "January 20",
+          "January 21",
+          "January 22",
+          "January 23",
+          "January 24",
+          "January 25",
+          "January 26",
+          "January 27",
+          "January 28",
+          "January 29",
+          "January 30",
+          "January 31",
+          "February 01",
+          "February 02",
+          "February 03",
+          "February 04",
+          "February 05",
+          "February 06",
+          "February 07",
+          "February 08",
+          "February 09",
+          "February 10",
+          "February 11",
+          "February 12",
+          "February 13",
+          "February 14",
+          "February 15",
+          "February 16",
+          "February 17",
+          "February 18",
+          "February 19",
+          "February 20",
+          "February 21",
+          "February 22",
+          "February 23",
+          "February 24",
+          "February 25",
+          "February 26",
+          "February 27",
+          "February 28",
+          "March 01",
+          "March 02",
+          "March 03",
+          "March 04",
+          "March 05",
+          "March 06",
+          "March 07",
+          "March 08",
+          "March 09",
+          "March 10",
+          "March 11",
+          "March 12",
+          "March 13",
+          "March 14",
+          "March 15",
+          "March 16",
+          "March 17",
+          "March 18",
+          "March 19",
+          "March 20",
+          "March 21",
+          "March 22",
+          "March 23",
+          "March 24",
+          "March 25",
+          "March 26",
+          "March 27",
+          "March 28",
+          "March 29",
+          "March 30",
+          "March 31",
+          "April 01",
+          "April 02",
+          "April 03",
+          "April 04",
+          "April 05",
+          "April 06",
+          "April 07",
+          "April 08",
+          "April 09",
+          "April 10",
+          "April 11",
+          "April 12",
+          "April 13",
+          "April 14",
+          "April 15",
+          "April 16",
+          "April 17",
+          "April 18",
+          "April 19",
+          "April 20",
+          "April 21",
+          "April 22",
+          "April 23",
+          "April 24",
+          "April 25",
+          "April 26",
+          "April 27",
+          "April 28",
+          "April 29",
+          "April 30",
+          "May 01",
+          "May 02",
+          "May 03",
+          "May 04",
+          "May 05",
+          "May 06",
+          "May 07",
+          "May 08",
+          "May 09",
+          "May 10",
+          "May 11",
+          "May 12",
+          "May 13",
+          "May 14",
+          "May 15",
+          "May 16",
+          "May 17",
+          "May 18",
+          "May 19",
+          "May 20",
+          "May 21",
+          "May 22",
+          "May 23",
+          "May 24",
+          "May 25",
+          "May 26",
+          "May 27",
+          "May 28",
+          "May 29",
+          "May 30",
+          "May 31",
+          "June 01",
+          "June 02",
+          "June 03",
+          "June 04",
+          "June 05",
+          "June 06",
+          "June 07",
+          "June 08",
+          "June 09",
+          "June 10",
+          "June 11",
+          "June 12",
+          "June 13",
+          "June 14",
+          "June 15",
+          "June 16",
+          "June 17",
+          "June 18",
+          "June 19",
+          "June 20",
+          "June 21",
+          "June 22",
+          "June 23",
+          "June 24",
+          "June 25",
+          "June 26",
+          "June 27",
+          "June 28",
+          "June 29",
+          "June 30",
+          "July 01",
+          "July 02",
+          "July 03",
+          "July 04",
+          "July 05",
+          "July 06",
+          "July 07",
+          "July 08",
+          "July 09",
+          "July 10",
+          "July 11",
+          "July 12",
+          "July 13",
+          "July 14",
+          "July 15",
+          "July 16",
+          "July 17",
+          "July 18",
+          "July 19",
+          "July 20",
+          "July 21",
+          "July 22",
+          "July 23",
+          "July 24",
+          "July 25",
+          "July 26",
+          "July 27",
+          "July 28",
+          "July 29",
+          "July 30",
+          "July 31",
+          "August 01",
+          "August 02",
+          "August 03",
+          "August 04",
+          "August 05",
+          "August 06",
+          "August 07",
+          "August 08",
+          "August 09",
+          "August 10",
+          "August 11",
+          "August 12",
+          "August 13",
+          "August 14",
+          "August 15",
+          "August 16",
+          "August 17",
+          "August 22",
+          "August 23",
+          "August 24",
+          "August 25",
+          "August 26",
+          "August 27",
+          "August 28",
+          "August 29",
+          "August 30",
+          "August 31",
+          "September 01",
+          "September 02",
+          "September 03",
+          "September 04",
+          "September 05",
+          "September 06",
+          "September 07",
+          "September 08",
+          "September 09",
+          "September 10",
+          "September 11",
+          "September 12",
+          "September 13",
+          "September 14",
+          "September 15",
+          "September 16",
+          "September 17",
+          "September 18",
+          "September 19",
+          "September 20",
+          "September 21",
+          "September 22",
+          "September 23",
+          "September 24",
+          "September 25",
+          "September 28",
+          "September 29",
+          "September 30",
+          "October 01",
+          "October 02",
+          "October 03",
+          "October 04",
+          "October 05",
+          "October 06",
+          "October 07",
+          "October 08",
+          "October 09",
+          "October 10",
+          "October 11",
+          "October 12",
+          "October 13",
+          "October 14",
+          "October 15",
+          "October 16",
+          "October 17",
+          "October 18",
+          "October 19",
+          "October 20",
+          "October 21",
+          "October 22",
+          "October 23",
+          "October 24",
+          "October 25",
+          "October 26",
+          "October 27",
+          "October 28",
+          "October 29",
+          "October 30",
+          "October 31",
+          "November 01",
+          "November 02",
+          "November 03",
+          "November 04",
+          "November 05",
+          "November 06",
+          "November 07",
+          "November 08",
+          "November 09",
+          "November 10",
+          "November 11",
+          "November 12",
+          "November 13",
+          "November 14",
+          "November 15",
+          "November 16",
+          "November 17",
+          "November 18",
+          "November 19",
+          "November 20",
+          "November 21",
+          "November 22",
+          "November 23",
+          "November 24",
+          "November 25",
+          "November 26"
+         ],
+         "xaxis": "x",
+         "y": [
+          0.3889888708756303,
+          0.37841772878960755,
+          0.3671661783939844,
+          0.3555531864561286,
+          0.3439136230141853,
+          0.3325887499711875,
+          0.32191641823656086,
+          0.31222124370234744,
+          0.3038050339247741,
+          0.2969377315297803,
+          0.29184912723494894,
+          0.28768391817769173,
+          0.28880679386572844,
+          0.29209946515708457,
+          0.2975082663404684,
+          0.3049167222001348,
+          0.3141473482602613,
+          0.32496509665221956,
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+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
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+            "linecolor": "white",
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+            "ticks": "",
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+           },
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+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
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+            "ticks": "",
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+            "ticks": ""
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "ds_year"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "yearly"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forecast_year = forecast_year.groupby([\"ds\",\"ds_year\"]).mean().reset_index()\n",
+    "px.area(forecast_year, x=\"ds_year\", y=\"yearly\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/movie_recommendation.ipynb b/notebooks/movie_recommendation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3d45f2c7b79086045d684c64ef830c4f6cedb90d
--- /dev/null
+++ b/notebooks/movie_recommendation.ipynb
@@ -0,0 +1,709 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Movie recommendation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os \n",
+    "import pickle\n",
+    "\n",
+    "path_data = r\"data/movies\"\n",
+    "\n",
+    "with open(os.path.join(path_data,'movies_dict.pkl'), 'rb') as file:\n",
+    "    movies_data = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "movies = pd.DataFrame(movies_data)\n",
+    "movies.drop_duplicates(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "def has_capital(string):\n",
+    "    for index, char in enumerate(string):\n",
+    "        if char.isupper() and index != 0:\n",
+    "            return True\n",
+    "    return False\n",
+    "\n",
+    "def clean_tags(text):\n",
+    "    pattern1 = re.compile(r'[?!]')\n",
+    "    pattern2 = re.compile(r'\\.(?!\\s|$)')\n",
+    "    pattern3 = re.compile(r'\\.[a-zA-Z]\\.')\n",
+    "    \n",
+    "    text_clean = re.sub(pattern1, '. ', text)\n",
+    "    text_clean = re.sub(pattern2, \"\", text_clean)\n",
+    "    text_clean = re.sub(pattern3, \"\", text_clean)\n",
+    "    text_clean = text_clean.replace(\"RobertDowneyJr.\",\"\").replace(\"SamuelL.\",\"\").replace(\"ScienceFiction\", \"Sciencefiction\")\n",
+    "\n",
+    "    tags_words = \" \".join([t for t in text_clean.split(\" \") if has_capital(t)==False])\n",
+    "    tags_words = [t for t in tags_words.split(\". \")[-1:][0].strip().split(\" \")[:8] if t!=\"\"]\n",
+    "    tags_words = [t for t in tags_words if t[0].isupper()==True]\n",
+    "    #tags_words_clean = [t for t in tags_words_clean if has_capital(t)==False]\n",
+    "    return \" \".join(sorted(tags_words)).replace(\"Sciencefiction\",\"Science Fiction\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies[\"tags\"] = movies[\"tags\"].apply(lambda x: x.replace(\"…\",\".\").replace(\"—\",\"\").replace(\"  \",\" \"))\n",
+    "movies[\"description\"] = movies[\"tags\"].apply(lambda x: \".\".join(x.split(\".\")[:-1] + [\"\"]))\n",
+    "movies[\"tags_clean\"] = movies[\"tags\"].apply(clean_tags).apply(lambda x: x.replace(\"Science Fiction\",\"Sciencefiction\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>movie_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>tags</th>\n",
+       "      <th>description</th>\n",
+       "      <th>tags_clean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19995</td>\n",
+       "      <td>Avatar</td>\n",
+       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
+       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
+       "      <td>Action Adventure Fantasy Sciencefiction</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>285</td>\n",
+       "      <td>Pirates of the Caribbean: At World's End</td>\n",
+       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
+       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
+       "      <td>Action Adventure Fantasy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>206647</td>\n",
+       "      <td>Spectre</td>\n",
+       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
+       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
+       "      <td>M While</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>49026</td>\n",
+       "      <td>The Dark Knight Rises</td>\n",
+       "      <td>Following the death of District Attorney Harve...</td>\n",
+       "      <td>Following the death of District Attorney Harve...</td>\n",
+       "      <td>Action Crime Drama Thriller</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49529</td>\n",
+       "      <td>John Carter</td>\n",
+       "      <td>John Carter is a war-weary, former military ca...</td>\n",
+       "      <td>John Carter is a war-weary, former military ca...</td>\n",
+       "      <td>Action Adventure Sciencefiction</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4804</th>\n",
+       "      <td>9367</td>\n",
+       "      <td>El Mariachi</td>\n",
+       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
+       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
+       "      <td>Action Crime Thriller</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4805</th>\n",
+       "      <td>72766</td>\n",
+       "      <td>Newlyweds</td>\n",
+       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
+       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
+       "      <td>Comedy Romance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4806</th>\n",
+       "      <td>231617</td>\n",
+       "      <td>Signed, Sealed, Delivered</td>\n",
+       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
+       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
+       "      <td>Comedy Drama Romance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4807</th>\n",
+       "      <td>126186</td>\n",
+       "      <td>Shanghai Calling</td>\n",
+       "      <td>When ambitious New York attorney Sam is sent t...</td>\n",
+       "      <td>When ambitious New York attorney Sam is sent t...</td>\n",
+       "      <td>Anonymous Written</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4808</th>\n",
+       "      <td>25975</td>\n",
+       "      <td>My Date with Drew</td>\n",
+       "      <td>Ever since the second grade when he first saw ...</td>\n",
+       "      <td>Ever since the second grade when he first saw ...</td>\n",
+       "      <td>Documentary</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4806 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      movie_id                                     title  \\\n",
+       "0        19995                                    Avatar   \n",
+       "1          285  Pirates of the Caribbean: At World's End   \n",
+       "2       206647                                   Spectre   \n",
+       "3        49026                     The Dark Knight Rises   \n",
+       "4        49529                               John Carter   \n",
+       "...        ...                                       ...   \n",
+       "4804      9367                               El Mariachi   \n",
+       "4805     72766                                 Newlyweds   \n",
+       "4806    231617                 Signed, Sealed, Delivered   \n",
+       "4807    126186                          Shanghai Calling   \n",
+       "4808     25975                         My Date with Drew   \n",
+       "\n",
+       "                                                   tags  \\\n",
+       "0     In the 22nd century, a paraplegic Marine is di...   \n",
+       "1     Captain Barbossa, long believed to be dead, ha...   \n",
+       "2     A cryptic message from Bond’s past sends him o...   \n",
+       "3     Following the death of District Attorney Harve...   \n",
+       "4     John Carter is a war-weary, former military ca...   \n",
+       "...                                                 ...   \n",
+       "4804  El Mariachi just wants to play his guitar and ...   \n",
+       "4805  A newlywed couple's honeymoon is upended by th...   \n",
+       "4806  \"Signed, Sealed, Delivered\" introduces a dedic...   \n",
+       "4807  When ambitious New York attorney Sam is sent t...   \n",
+       "4808  Ever since the second grade when he first saw ...   \n",
+       "\n",
+       "                                            description  \\\n",
+       "0     In the 22nd century, a paraplegic Marine is di...   \n",
+       "1     Captain Barbossa, long believed to be dead, ha...   \n",
+       "2     A cryptic message from Bond’s past sends him o...   \n",
+       "3     Following the death of District Attorney Harve...   \n",
+       "4     John Carter is a war-weary, former military ca...   \n",
+       "...                                                 ...   \n",
+       "4804  El Mariachi just wants to play his guitar and ...   \n",
+       "4805  A newlywed couple's honeymoon is upended by th...   \n",
+       "4806  \"Signed, Sealed, Delivered\" introduces a dedic...   \n",
+       "4807  When ambitious New York attorney Sam is sent t...   \n",
+       "4808  Ever since the second grade when he first saw ...   \n",
+       "\n",
+       "                                   tags_clean  \n",
+       "0     Action Adventure Fantasy Sciencefiction  \n",
+       "1                    Action Adventure Fantasy  \n",
+       "2                                     M While  \n",
+       "3                 Action Crime Drama Thriller  \n",
+       "4             Action Adventure Sciencefiction  \n",
+       "...                                       ...  \n",
+       "4804                    Action Crime Thriller  \n",
+       "4805                           Comedy Romance  \n",
+       "4806                     Comedy Drama Romance  \n",
+       "4807                        Anonymous Written  \n",
+       "4808                              Documentary  \n",
+       "\n",
+       "[4806 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "import numpy as np\n",
+    "\n",
+    "count_genre = pd.Series([t_ for t in movies[\"tags_clean\"].to_list() for t_ in t.split(\" \")]).value_counts().to_frame()\n",
+    "list_genres = list(count_genre.loc[count_genre[\"count\"]>75].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# index of movies with wrong tags\n",
+    "list_index = []\n",
+    "for index, t in enumerate(movies[\"tags_clean\"].to_list()):\n",
+    "    for elem in t.split():\n",
+    "        if elem not in list_genres:\n",
+    "            list_index.append(index)\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict_tags = dict()\n",
+    "for index, description in zip(list_index, movies.iloc[list_index][\"tags\"].to_list()):\n",
+    "    list_tags = []    \n",
+    "    for genre in list_genres:\n",
+    "        if genre in description: \n",
+    "            list_tags.append(genre)\n",
+    "        dict_tags[index] = \" \".join(list_tags)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_9060\\521199459.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())\n"
+     ]
+    }
+   ],
+   "source": [
+    "movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies.drop(columns=\"tags\",inplace=True)\n",
+    "movies.rename({\"tags_clean\":\"genre\"},axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies[\"genre\"] = movies[\"genre\"].apply(lambda x:x.replace(\" \",\", \").replace(\"Sciencefiction\", \"Science Fiction\").replace(\"–\",\" \"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>movie_id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>description</th>\n",
+       "      <th>genre</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>99861</td>\n",
+       "      <td>Avengers: Age of Ultron</td>\n",
+       "      <td>When Tony Stark tries to jumpstart a dormant p...</td>\n",
+       "      <td>Action, Adventure, Science Fiction</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   movie_id                    title  \\\n",
+       "7     99861  Avengers: Age of Ultron   \n",
+       "\n",
+       "                                         description  \\\n",
+       "7  When Tony Stark tries to jumpstart a dormant p...   \n",
+       "\n",
+       "                                genre  \n",
+       "7  Action, Adventure, Science Fiction  "
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies.loc[movies[\"title\"]==\"Avengers: Age of Ultron\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'When Tony Stark tries to jumpstart a dormant peacekeeping program, things go awry and Earth’s Mightiest Heroes are put to the ultimate test as the fate of the planet hangs in the balance. As the villainous Ultron emerges, it is up to The Avengers to stop him from enacting his terrible plans, and soon uneasy alliances and unexpected action pave the way for an epic and unique global adventure. Action Adventure ScienceFiction marvelcomic sequel superhero basedoncomicbook vision superheroteam duringcreditsstinger marvelcinematicuniverse 3d RobertDowneyJr.'"
+      ]
+     },
+     "execution_count": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies[\"description\"].to_list()[7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_description_v2(text):\n",
+    "    new_text = text.split(\". \")[-1]\n",
+    "    for genre in list_genres:\n",
+    "        if genre in new_text:\n",
+    "            return \". \".join(text.split(\". \")[:-1] + [\"\"]).strip()\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies[\"description\"] = movies[\"description\"].apply(clean_description_v2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies.to_pickle(\"data/movies/movies_dict2.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vote_info = pickle.load(open(os.path.join(path_data,\"vote_info.pkl\"),\"rb\"))\n",
+    "vote = pd.DataFrame(vote_info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies.rename({\"movie_id\":\"id\"}, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>title</th>\n",
+       "      <th>description</th>\n",
+       "      <th>genre</th>\n",
+       "      <th>vote_average</th>\n",
+       "      <th>vote_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19995</td>\n",
+       "      <td>Avatar</td>\n",
+       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
+       "      <td>Action, Adventure, Fantasy, Science Fiction</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>11800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>285</td>\n",
+       "      <td>Pirates of the Caribbean: At World's End</td>\n",
+       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
+       "      <td>Action, Adventure, Fantasy</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>4500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>206647</td>\n",
+       "      <td>Spectre</td>\n",
+       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
+       "      <td>Action, Adventure, Crime</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>4466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>49026</td>\n",
+       "      <td>The Dark Knight Rises</td>\n",
+       "      <td>Following the death of District Attorney Harve...</td>\n",
+       "      <td>Action, Crime, Drama, Thriller</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>9106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49529</td>\n",
+       "      <td>John Carter</td>\n",
+       "      <td>John Carter is a war-weary, former military ca...</td>\n",
+       "      <td>Action, Adventure, Science Fiction</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>2124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4801</th>\n",
+       "      <td>9367</td>\n",
+       "      <td>El Mariachi</td>\n",
+       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
+       "      <td>Action, Crime, Thriller</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4802</th>\n",
+       "      <td>72766</td>\n",
+       "      <td>Newlyweds</td>\n",
+       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
+       "      <td>Comedy, Romance</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4803</th>\n",
+       "      <td>231617</td>\n",
+       "      <td>Signed, Sealed, Delivered</td>\n",
+       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
+       "      <td>Comedy, Drama, Romance</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4804</th>\n",
+       "      <td>126186</td>\n",
+       "      <td>Shanghai Calling</td>\n",
+       "      <td>When ambitious New York attorney Sam is sent t...</td>\n",
+       "      <td></td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4805</th>\n",
+       "      <td>25975</td>\n",
+       "      <td>My Date with Drew</td>\n",
+       "      <td>Ever since the second grade when he first saw ...</td>\n",
+       "      <td>Documentary</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4806 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id                                     title  \\\n",
+       "0      19995                                    Avatar   \n",
+       "1        285  Pirates of the Caribbean: At World's End   \n",
+       "2     206647                                   Spectre   \n",
+       "3      49026                     The Dark Knight Rises   \n",
+       "4      49529                               John Carter   \n",
+       "...      ...                                       ...   \n",
+       "4801    9367                               El Mariachi   \n",
+       "4802   72766                                 Newlyweds   \n",
+       "4803  231617                 Signed, Sealed, Delivered   \n",
+       "4804  126186                          Shanghai Calling   \n",
+       "4805   25975                         My Date with Drew   \n",
+       "\n",
+       "                                            description  \\\n",
+       "0     In the 22nd century, a paraplegic Marine is di...   \n",
+       "1     Captain Barbossa, long believed to be dead, ha...   \n",
+       "2     A cryptic message from Bond’s past sends him o...   \n",
+       "3     Following the death of District Attorney Harve...   \n",
+       "4     John Carter is a war-weary, former military ca...   \n",
+       "...                                                 ...   \n",
+       "4801  El Mariachi just wants to play his guitar and ...   \n",
+       "4802  A newlywed couple's honeymoon is upended by th...   \n",
+       "4803  \"Signed, Sealed, Delivered\" introduces a dedic...   \n",
+       "4804  When ambitious New York attorney Sam is sent t...   \n",
+       "4805  Ever since the second grade when he first saw ...   \n",
+       "\n",
+       "                                            genre  vote_average  vote_count  \n",
+       "0     Action, Adventure, Fantasy, Science Fiction           7.2       11800  \n",
+       "1                      Action, Adventure, Fantasy           6.9        4500  \n",
+       "2                        Action, Adventure, Crime           6.3        4466  \n",
+       "3                  Action, Crime, Drama, Thriller           7.6        9106  \n",
+       "4              Action, Adventure, Science Fiction           6.1        2124  \n",
+       "...                                           ...           ...         ...  \n",
+       "4801                      Action, Crime, Thriller           6.6         238  \n",
+       "4802                              Comedy, Romance           5.9           5  \n",
+       "4803                       Comedy, Drama, Romance           7.0           6  \n",
+       "4804                                                        5.7           7  \n",
+       "4805                                  Documentary           6.3          16  \n",
+       "\n",
+       "[4806 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies.merge(vote, on=\"id\", how=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pages/object_detection.py b/pages/object_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..7341b7c556259b8896f2f086f5b862e77f66b9b1
--- /dev/null
+++ b/pages/object_detection.py
@@ -0,0 +1,297 @@
+import torch
+import os
+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import altair as alt
+
+from PIL import Image
+from transformers import YolosFeatureExtractor, YolosForObjectDetection
+from torchvision.transforms import ToTensor, ToPILImage
+
+st.set_page_config(layout="wide")
+
+
+def rgb_to_hex(rgb):
+    """Converts an RGB tuple to an HTML-style Hex string."""
+    hex_color = "#{:02x}{:02x}{:02x}".format(int(rgb[0] * 255), int(rgb[1] * 255), int(rgb[2] * 255))
+    return hex_color
+
+## CODE TO CLEAN IMAGES
+def fix_channels(t):
+    if len(t.shape) == 2:
+        return ToPILImage()(torch.stack([t for i in (0, 0, 0)]))
+    if t.shape[0] == 4:
+        return ToPILImage()(t[:3])
+    if t.shape[0] == 1:
+        return ToPILImage()(torch.stack([t[0] for i in (0, 0, 0)]))
+    return ToPILImage()(t)
+
+## CODE FOR PLOTS WITH BOUNDING BOXES
+COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
+        [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]
+
+def idx_to_text(i):
+    if i in list(dict_cats_final.keys()):
+        return dict_cats_final[i.item()]
+    else:
+        return False
+
+# for output bounding box post-processing
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
+            (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=1)
+
+def rescale_bboxes(out_bbox, size):
+    img_w, img_h = size
+    b = box_cxcywh_to_xyxy(out_bbox)
+    b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
+    return b
+
+def plot_results(pil_img, prob, boxes):
+    fig = plt.figure(figsize=(16,10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    
+    colors = COLORS * 100
+    colors_used = []
+
+    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
+        cl = p.argmax()
+        p_max = p.max().detach().numpy()
+        if idx_to_text(cl) is False:
+            pass
+            
+        else:    
+            colors_used.append(rgb_to_hex(c))
+            ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
+                                    fill=False, color=c, linewidth=3))
+            ax.text(xmin, ymin, f"{idx_to_text(cl)}", fontsize=10,
+                    bbox=dict(facecolor=c, alpha=0.8))
+            
+    plt.axis('off')
+    
+    plt.savefig("results_od.png", 
+            bbox_inches ="tight") 
+    #plt.show()
+    st.image("results_od.png")
+
+    return colors_used
+
+
+def return_probas(outputs, threshold):
+    probas = outputs.logits.softmax(-1)[0, :, :-1]
+    probas = probas[:][:,list(dict_cats_final.keys())]
+    keep = probas.max(-1).values > threshold
+
+    return probas, keep
+
+
+
+# def visualize_predictions(image, outputs, threshold):
+# # keep only predictions with confidence >= threshold
+#     # convert predicted boxes from [0; 1] to image scales
+#     bboxes_scaled = rescale_bboxes(outputs.pred_boxes[0, keep].cpu(), image.size)
+
+#     # plot results
+#     plot_results(image, probas[keep], bboxes_scaled)
+
+#     return probas[keep]
+
+
+def visualize_probas(probas, threshold, colors):
+    label_df = pd.DataFrame({"label":probas.max(-1).indices.detach().numpy(),
+                             "proba":probas.max(-1).values.detach().numpy()})
+    
+    cats_dict = dict(zip(np.arange(0,len(cats)),cats))
+    label_df["label"] = label_df["label"].map(cats_dict)
+    top_label_df = label_df.loc[label_df["proba"]>threshold].round(2)
+    top_label_df["colors"] = colors
+    top_label_df.sort_values(by=["proba"], ascending=False, inplace=True)
+
+    st.dataframe(top_label_df.drop(columns=["colors"]))
+    
+    mode_func = lambda x: x.mode().iloc[0]
+    top_label_df_agg = top_label_df.groupby("label").agg({"proba":"mean", "colors":mode_func})
+    top_label_df_agg = top_label_df_agg.reset_index().sort_values(by=["proba"], ascending=False)
+
+    chart = alt.Chart(top_label_df_agg).mark_bar().encode(x="proba", y="label", 
+                                                          color=alt.Color('colors:N', scale=None)).interactive()
+    #st.altair_chart(chart)
+
+
+
+
+
+######################################################################################################################################
+
+st.markdown("# Object Detection")
+
+st.markdown("### What is Object Detection ?")
+           
+#st.markdown("""Object detection involves **identifying** and **locating objects** within an image or video frame through bounding boxes. """)
+st.info("""Object Detection is a computer vision task in which the goal is to **detect** and **locate objects** of interest in an image or video. 
+            The task involves identifying the position and boundaries of objects (or **bounding boxes**) in an image, and classifying the objects into different categories.""")
+
+
+st.markdown("Here is an example of Object Detection for Traffic Analysis.")
+#image_od = Image.open('images/od_2.png')
+#st.image(image_od, width=600)
+st.video(data='https://www.youtube.com/watch?v=PVCGDoTZHaI')
+
+st.markdown(" ")
+
+st.markdown("""Common applications of Object Detection include:
+- **Autonomous Vehicles** :car: : Object detection is crucial for self-driving cars to track pedestrians, cyclists, other vehicles, and obstacles on the road.
+- **Retail** 🏬 : Implementing smart shelves and checkout systems that use object detection to track inventory and monitor stock levels.
+- **Healthcare** 👨‍⚕️: Detecting and tracking anomalies in medical images, such as tumors or abnormalities, for diagnostic purposes or prevention.
+- **Manufacturing** 🏭: Quality control on production lines by detecting defects or irregularities in manufactured products. Ensuring workplace safety by monitoring the movement of workers and equipment.
+- **Fashion and E-commerce** 🛍️ : Improving virtual try-on experiences by accurately detecting and placing virtual clothing items on users.
+""")
+
+
+st.markdown("  ")
+st.divider()
+
+st.markdown("### Fashion object detection 👗")
+st.markdown(""" The following example showcases the use of an **Object detection algorithm** for clothing items/features on fashion images. <br>
+            This use case can be seen as an application of AI models for Fashion and E-commerce. <br>
+            """, unsafe_allow_html=True)
+
+st.image("images/od_fashion.jpg", width=700)
+
+#images_dior = [os.path.join("data/dior_show",url) for url in os.listdir("data/dior_show") if url != "results"]
+#st.image(images_dior, width=250, caption=[file for file in os.listdir("data/dior_show") if file != "results"])
+
+st.markdown("  ")
+#st.markdown("##### Select an image")
+
+
+############## SELECT AN IMAGE ###############
+
+st.markdown("#### Step 1: Select an image")
+st.markdown("""First, select the image you want to apply the object detection model to. 
+            The model was trained to detect clothing items on a single person. If your image has more than individuals, the model will ignore one of them in its detection.""")
+
+image_ = None
+select_image_box = st.radio(
+    "",
+    ["Choose an existing image", "Load your own image"],
+    index=None, label_visibility="collapsed")
+
+if select_image_box == "Choose an existing image":
+    fashion_images_path = r"data/pinterest"
+    list_images = os.listdir(fashion_images_path)
+    image_ = st.selectbox("", list_images, label_visibility="collapsed")
+    
+    if image_ is not None:
+        image_ = os.path.join(fashion_images_path,image_)
+        st.markdown("You've selected the following image:")
+        st.image(image_, width=300)
+
+elif select_image_box == "Load your own image":
+    image_ = st.file_uploader("Load an image here", 
+                                key="OD_dior", type=['jpg','jpeg','png'], label_visibility="collapsed")
+    
+    st.warning("""**Note**: The model tends to perform better with images of people/clothing items facing forward. 
+           Choose this type of image if you want optimal results.""")
+
+    if image_ is not None:
+        st.image(Image.open(image_), width=300)
+
+
+st.markdown("  ")
+st.markdown("  ")
+
+
+
+########## SELECT AN ELEMENT TO DETECT ##################
+
+cats = ['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit',
+    'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar',
+    'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']
+
+dict_cats = dict(zip(np.arange(len(cats)), cats))
+
+st.markdown("#### Step 2: Choose the elements you want to detect")
+
+# Select one or more elements to detect
+container = st.container()
+selected_options = None
+all = st.checkbox("Select all")
+
+if all:
+    selected_options = container.multiselect("**Select one or more items**", cats, cats)
+else:
+    selected_options = container.multiselect("**Select one or more items**", cats)
+
+#cats = selected_options 
+dict_cats_final = {key:value for (key,value) in dict_cats.items() if value in selected_options}
+
+
+st.markdown("  ")
+st.markdown("  ")
+
+
+
+############## SELECT A THRESHOLD ###############
+
+st.markdown("#### Step 3: Select a threshold")
+
+st.markdown("""Finally, select a threshold for the model. 
+            The threshold helps you decide how confident you want your model to be with its predictions. 
+            Elements that were identified with a lower probability than the given threshold will be ignored in the final results.""")
+
+threshold = st.slider('**Select a threshold**', min_value=0.0, step=0.05, max_value=1.0, value=0.75, label_visibility="collapsed")
+# min_value=0.000000, step=0.000001, max_value=0.0005, value=0.0000045, format="%f"
+
+if threshold < 0.6:
+    st.warning("""**Warning**: Selecting a low threshold (below 0.6) could lead the model to make errors and detect too many objects.""")
+
+st.write("You've selected a threshold at", threshold)
+
+
+st.markdown("  ")
+
+
+############# RUN MODEL ################
+
+run_model = st.button("**Run the model**", type="primary")
+
+if run_model:
+    if image_ != None and selected_options != None and threshold!= None:
+        with st.spinner('Wait for it...'):
+            ## SELECT IMAGE
+            image = Image.open(image_)
+            image = fix_channels(ToTensor()(image))
+
+            ## LOAD OBJECT DETECTION MODEL
+            MODEL_NAME = "valentinafeve/yolos-fashionpedia"
+            feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small')
+            model = YolosForObjectDetection.from_pretrained(MODEL_NAME)
+
+            # RUN MODEL ON IMAGE
+            inputs = feature_extractor(images=image, return_tensors="pt")
+            outputs = model(**inputs)
+            probas, keep = return_probas(outputs, threshold)
+
+            # PLOT BOUNDING BOX AND BARS/PROBA
+            col1, col2 = st.columns(2)
+            with col1:
+                st.markdown("##### Bounding box results")
+                bboxes_scaled = rescale_bboxes(outputs.pred_boxes[0, keep].cpu(), image.size)
+                colors_used = plot_results(image, probas[keep], bboxes_scaled)
+            
+            with col2: 
+                visualize_probas(probas, threshold, colors_used)
+            
+            st.info("Done")
+
+    else:
+        st.warning("You must select an **image**, **elements to detect** and a **threshold** to run the model !")
+
+
+    
\ No newline at end of file
diff --git a/pages/recommendation_system.py b/pages/recommendation_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbcc99d71e3c66c8936f8284cae2f8df57d53c5f
--- /dev/null
+++ b/pages/recommendation_system.py
@@ -0,0 +1,451 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import requests
+import pickle
+import os
+import altair as alt
+import plotly.express as px
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics.pairwise import cosine_similarity
+from annotated_text import annotated_text
+from utils import load_data_pickle, load_model_pickle, load_data_csv
+
+
+
+st.set_page_config(layout="wide")
+
+
+
+st.markdown("# Recommendation system")
+
+st.markdown("### What is a Recommendation System ?")
+
+st.info("""**Recommendation systems** are AI algorithms built to **suggest** or **recommend** **products** to consumers.
+        They are very common in social media platforms such as TikTok, Youtube or Instagram or e-commerce websites as they help improve and personalize a consumer's experience.""")
+
+st.markdown("""There are two methods to build recommendation systems:
+- **Content-based filtering**: Recommendations are made based on the user's own preferences
+- **Collaborative filtering**: Recommendations are made based on the preferences and behavior of similar users""", unsafe_allow_html=True)
+            
+# st.markdown("""Here is an example of **Content-based filtering versus Collaborative filtering** for movie recommendations.""")
+st.markdown(" ")
+st.markdown(" ")
+
+_, col_img, _ = st.columns(spec=[0.2,0.6,0.2])
+with col_img:
+    st.image("images/rs.png")
+
+st.markdown(" ")
+
+st.markdown("""Common applications of Recommendation systems include:
+- **E-Commerce Platforms** 🛍️: Suggest products to users based on their browsing history, purchase patterns, and preferences. 
+- **Streaming Services** 📽️: Recommend movies, TV shows, or songs based on users' viewing/listening history and preferences. 
+- **Social Media Platforms** 📱: Suggest friends, groups, or content based on users' connections, interests, and engagement history.
+- **Automotive and Navigation Systems** 🗺️: Suggest optimal routes based on real-time traffic conditions, historical data, and user preferences.   
+""")
+
+st.markdown(" ")
+
+select_usecase = st.selectbox("**Choose a use case**", 
+                              ["Movie recommendation system 📽️", 
+                               "Hotel recommendation system 🛎️"])
+
+st.divider()
+
+
+
+#####################################################################################################
+#                                       MOVIE RECOMMENDATION SYSTEM                                 #
+#####################################################################################################
+
+# Recommendation function
+def recommend(movie_name, nb):
+    n_movies_to_recommend = nb
+    idx = movies[movies['title'] == movie_name].index[0]
+
+    distances, indices = model.kneighbors(csr_data[idx], n_neighbors=n_movies_to_recommend + 1)
+    idx = list(indices.squeeze())
+    df = np.take(movies, idx, axis=0)
+
+    movies_list = list(df.title[1:])
+
+    recommend_movies_names = []
+    recommend_posters = []
+    movie_ids = []
+    for i in movies_list:
+        temp_movie_id = (movies[movies.title ==i].movie_id).values[0]
+        movie_ids.append(temp_movie_id)
+        
+        # fetch poster
+        try:
+            poster = fetch_poster(temp_movie_id)
+            recommend_posters.append(poster)
+        except:
+            recommend_posters.append(None)
+        
+        recommend_movies_names.append(i)
+    return recommend_movies_names, recommend_posters, movie_ids
+
+# Get poster
+def fetch_poster(movie_id):
+    response = requests.get(f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}')
+    data = response.json()
+    return "https://image.tmdb.org/t/p/w500/" + data["poster_path"]
+
+
+
+if select_usecase == "Movie recommendation system 📽️":
+
+    colors = ["#8ef", "#faa", "#afa", "#fea", "#8ef","#afa"]
+    api_key = st.secrets["recommendation_system"]["key"]
+
+    # Load data 
+    path_data = r"data/movies"
+    path_models = r"pretrained_models/recommendation_system"
+
+    movies_dict = pickle.load(open(os.path.join(path_data,"movies_dict2.pkl"),"rb"))
+    movies = pd.DataFrame(movies_dict)
+    movies.drop_duplicates(inplace=True)
+
+    vote_info = pickle.load(open(os.path.join(path_data,"vote_info.pkl"),"rb"))
+    vote = pd.DataFrame(vote_info)
+
+    # Load model
+    model = load_model_pickle(path_models,"model.pkl")
+    with open(os.path.join(path_data,'csr_data_tf.pkl'), 'rb') as file:
+        csr_data = pickle.load(file)
+
+
+    # Description of the use case
+    st.markdown("""## Movie Recommendation System 📽️""")
+
+    #st.info(""" """)
+
+    st.markdown("""This use case showcases the use of recommender systems for **movie recommendations** using **collaborative filtering**. <br>
+                   The model recommends and ranks movies based on what users, who have also watched the chosen movie, have watched else on the platform. <br> 
+    """, unsafe_allow_html=True)
+    st.markdown(" ")
+
+    
+    # User selection
+    selected_movie = st.selectbox("**Select a movie**", movies["title"].values[:-3])
+    selected_nb_movies = st.selectbox("**Select a number of movies to recommend**", np.arange(2,7), index=3)
+
+    # Show user selection on the app
+    c1, c2 = st.columns([0.7,0.3], gap="medium")
+    with c1:
+        new_movies = movies.rename({"movie_id":"id"},axis=1).merge(vote, on="id", how="left")
+        description = new_movies.loc[new_movies["title"]==selected_movie,"description"].to_list()[0]
+        genre = new_movies.loc[new_movies["title"]==selected_movie,"genre"].to_list()[0]
+        vote_ = new_movies.loc[new_movies["title"]==selected_movie,"vote_average"].to_list()[0]
+        vote_count = new_movies.loc[new_movies["title"]==selected_movie,"vote_count"].to_list()[0]
+        
+        list_genres = [(g.strip(),"",color) for color,g in zip(colors, genre.split(", "))]
+        
+        st.header(selected_movie, divider="grey")
+        st.markdown(f"**Synopsis**: {description}")
+        annotated_text(["**Genre(s)**: ", list_genres])
+        st.markdown(f"**Rating**: {vote_}:star:")
+        st.markdown(f"**Votes**: {vote_count}")
+
+        st.info(f"You've selected {selected_nb_movies} movies to recommend")
+        st.markdown(" ")
+        
+        recommend_button = st.button("**Recommend movies**")
+
+    with c2:
+        try:
+            poster = fetch_poster(movies.loc[movies["title"]==selected_movie,"movie_id"].to_list()[0])
+            st.image(poster, width=300)
+        except:
+            pass
+
+
+    # Run model and show results
+    if recommend_button:
+        st.text("Here are few Recommendations..")
+        names,posters,movie_ids = recommend(selected_movie, selected_nb_movies)
+        tab1, tab2 = st.tabs(["View movies", "View genres"])
+
+        with tab1:
+            cols=st.columns(int(selected_nb_movies))
+            #cols=[col1,col2,col3,col4,col5]
+            for i in range(0,selected_nb_movies):
+                with cols[i]:
+                    expander = st.expander("See movie details")
+                    
+                    if posters[i] == None:
+                        pass
+                    else:
+                        st.image(posters[i])
+                    
+                    st.markdown(f"##### **{i+1}. {names[i]}**")
+                    id = movie_ids[i]
+
+                    genre = movies.loc[movies["movie_id"]==id,"genre"].to_list()[0]
+                    list_genres = [(g.strip(),"",color) for color,g in zip(colors, genre.split(", "))]
+
+                    synopsis = movies.loc[movies['movie_id']==id, "description"].to_list()[0]
+                    st.markdown(synopsis)
+
+                    vote_avg, vote_count = vote[vote["id"] == id].vote_average , vote[vote["id"] == id].vote_count
+                    annotated_text(["**Genre(s)**: ", list_genres])
+                    st.markdown(f"""**Rating**: {list(vote_avg.values)[0]}:star:""")
+                    st.markdown(f"**Votes**: {list(vote_count.values)[0]}")
+        
+        
+        with tab2:
+            recommended_genres = movies.loc[movies["movie_id"].isin(movie_ids[:5]),"genre"].to_list()
+            list_recom_genres = [genre for list_genres in recommended_genres for genre in list_genres.split(", ")]
+            df_recom_genres = pd.Series(list_recom_genres).value_counts().to_frame().reset_index(names="genre")
+            df_recom_genres["proportion (%)"] = (100*df_recom_genres["count"]/df_recom_genres["count"].sum())
+
+            fig = px.bar(df_recom_genres, x='count', y='genre', color="genre", title='Most recommended genres', orientation="h")
+            st.plotly_chart(fig, use_container_width=True)
+
+
+
+
+
+
+
+#####################################################################################################
+#                                       HOTEL RECOMMENDATION SYSTEM                                 #
+#####################################################################################################
+            
+
+# Load scaler with caching
+    
+    
+        
+if select_usecase == "Hotel recommendation system 🛎️":
+
+    @st.cache_data(ttl=3600)
+    def get_scaler(df):
+        scaler = MinMaxScaler()
+        scaler.fit(df[['Rating', 'Price']])
+        return scaler
+
+    def recommend_hotels_with_location_and_beds(df, preferences, max_recommendations=5):
+        # Start with the full dataset
+        filtered_df = df.copy()
+        
+        # Filter by Location if specified (either city or country)
+        if 'Location' in preferences and preferences['Location']:
+            filtered_df = filtered_df[(filtered_df['City'].str.contains(preferences['Location'], case=False, na=False)) |
+                                    (filtered_df['Country'].str.contains(preferences['Location'], case=False, na=False))]
+
+        # Filter by Number of beds if specified
+        if 'Number of beds' in preferences:
+            filtered_df = filtered_df[filtered_df['Number of bed'] == preferences['Number of beds']]
+        
+        # Filter by Rating if specified
+        if 'Rating' in preferences:
+            min_rating, max_rating = preferences['Rating']
+            filtered_df = filtered_df[filtered_df['Rating'].between(min_rating, max_rating)]
+        
+        # Filter by Price range if specified
+        if 'Price' in preferences:
+            min_price, max_price = preferences['Price']
+            filtered_df = filtered_df[filtered_df['Price'].between(min_price, max_price)]
+
+        # Ensure there are still hotels after filtering
+        if filtered_df.empty:
+            # Send a notification if no hotels match the criteria
+            send_notification("No hotels were found matching the specified criteria.")
+            return pd.DataFrame(), "No hotels were found matching the specified criteria."
+        
+        preferences["Rating"] = np.mean(np.array(preferences["Rating"]))
+        preferences["Price"] = np.mean(np.array(preferences["Price"]))
+
+        # Normalize the preferences vector (excluding location and number of beds for similarity calculation)
+        preferences_vector = np.array([[preferences.get('Rating', 0),
+                                        preferences.get('Price', 0)]])
+        preferences_vector_normalized = scaler.transform(preferences_vector)
+
+        # Calculate similarity scores for the filtered hotels
+        filtered_numerical_features = filtered_df[['Rating', 'Price']]
+        filtered_numerical_features_normalized = scaler.transform(filtered_numerical_features)
+        similarity_scores = cosine_similarity(preferences_vector_normalized, filtered_numerical_features_normalized)[0]
+
+        # Get the indices of top_n similar hotels
+        top_indices = similarity_scores.argsort()[-max_recommendations:][::-1]
+        recommended_indices = filtered_df.iloc[top_indices].index
+
+        # Return the recommended hotels with relevant details (including specified columns)
+        return df.loc[recommended_indices], None
+
+
+    def send_notification(message):
+        """
+        Placeholder function to send a notification.
+        This function can be replaced with the actual notification mechanism (e.g., email, SMS).
+        """
+        print("Notification:", message)
+
+
+    def country_info(country):
+        if country == "Thailand":
+            image = "images/thailand.jpeg"
+            emoji = "🏝️"
+            description = """**Description**: 
+Thailand seamlessly fuses ancient traditions with modern dynamism, creating an unparalleled tapestry for travelers. 
+Renowned for its warm hospitality, vibrant culture, and delectable cuisine, Thailand offers an unforgettable experience for every adventurer."""
+            top_places = """
+- **Bangkok**: Immerse yourself in the hustle and bustle of Bangkok's streets, adorned with glittering temples and bustling markets. The Grand Palace and Khao San Road showcase the city's unique blend of tradition and modernity.
+- **Chiang Mai**: Nestled in the misty mountains of Northern Thailand, Chiang Mai captivates with ancient temples, lush landscapes, and vibrant night markets. The Old City exudes a unique atmosphere, while the surrounding hills offer tranquility.
+- **Phuket**: Thailand's largest island, Phuket, beckons beach lovers with its stunning white sands, vibrant nightlife, and water activities. It's a perfect blend of relaxation and excitement."""
+
+        if country == "France":
+            image = "images/france.jpeg"
+            emoji = "⚜️"
+            description ="""**Description**:
+Indulge in the countries rich tapestry of art, culture, and gastronomy. 
+From the romantic allure of Paris to the sun-kissed vineyards of Provence, every corner of this diverse country tells a unique story, promising an unforgettable journey for every traveler."""
+            top_places = """ 
+- **Paris**: Dive into the city's iconic landmarks such as the Eiffel Tower, Louvre Museum, and Notre-Dame Cathedral grace the skyline.
+- **Provence**: Visit the stunning Palais des Papes in Avignon, explore the colorful markets of Aix-en-Provence, and unwind in the serene beauty of the Luberon region.
+- **Côte d'Azur**: This stunning stretch of the French coastline is a captivating blend of azure waters, picturesque landscapes and charming villages.
+"""
+
+        if country == "Spain":
+            image = "images/spain-banner.jpg"
+            emoji = "☀️"
+            description = """**Description**:
+Embark on an unforgettable journey where tradition and modernity coexist in harmony. 
+From the lively streets of Barcelona to the sun-soaked beaches of Andalusia, Spain offers a captivating blend of history, culture, and natural beauty.            
+"""
+            top_places = """
+- **Barcelona**: Explore the iconic Sagrada Familia, stroll down the vibrant La Rambla, and soak in the Mediterranean ambiance at Barceloneta Beach.
+- **Seville**: Visit the awe-inspiring Alcázar, marvel at the Giralda Tower, and wander through the enchanting alleys of the Santa Cruz neighborhood.
+- **Granada**: Explore the Generalife Gardens, stroll through the Albayzín quarter with its narrow streets and white houses, and savor the views of the city from the Mirador de San Nicolás.
+"""
+
+        if country == "Singapore":
+            image = "images/singapore.jpg"
+            emoji = "🏙️"
+            description = """**Description**:
+From gleaming skyscrapers to vibrant neighborhoods, this cosmopolitan gem in Southeast Asia promises an immersive journey into a world where tradition meets cutting-edge technology."""
+
+            top_places = """
+- **Marina Bay Sands**: Enjoy panoramic views from the SkyPark, take a dip in the infinity pool, and explore The Shoppes for luxury shopping and entertainment. At night, witness the mesmerizing light and water show at the Marina Bay Sands Skypark.
+- **Gardens by the Bay**: Explore the Flower Dome and Cloud Forest conservatories, and stroll through the scenic OCBC Skyway for breathtaking views of the gardens and city.
+- **Sentosa Island**: Escape to Sentosa Island, a resort destination offering a myriad of attractions. Relax on pristine beaches, visit Universal Studios Singapore for thrilling rides, and explore S.E.A. Aquarium for an underwater adventure.
+
+"""
+
+        ###### STREAMLIT MARKDOWN ######
+        st.header(f"{country} {emoji}", divider="grey")
+        st.image(image)
+        st.markdown(description)
+
+        see_top_places = st.checkbox("**Top places to visit**", key={country})
+        if see_top_places:
+            st.markdown(top_places)
+
+    st.markdown("""## Hotel Recommendation System 🛎️""")
+
+    st.info("""This use case shows how you can create personalized hotel recommendations using a recommendation system with **content-based Filtering**. 
+                Analyzing location, amenities, price, and reviews, the model suggests tailored hotel recommendation based on the user's preference.
+    """)
+    st.markdown(" ")
+
+
+    path_hotels_data = r"data/hotels"
+
+    # Load hotel data
+    df = load_data_csv(path_hotels_data,"booking_df.csv")
+
+    # clean data
+    df.drop_duplicates(inplace=True)
+    df["Country"] = df["Country"].apply(lambda x: "Spain" if x=="Espagne" else x)
+    list_cities = df["City"].value_counts().to_frame().reset_index()
+    list_cities = list_cities.loc[list_cities["count"]>=5,"City"].to_numpy()
+    df = df.loc[(df["City"].isin(list_cities)) & (df["Number of bed"]<=6)]
+    df["Price"] = df["Price"].astype(int)
+    df.loc[(df["Number of bed"]==0) & (df["Price"]<1000),"Number of bed"] = 1
+    df.loc[(df["Number of bed"]==0) & (df["Price"].between(1000,2000)),"Number of bed"] = 2
+    df.loc[(df["Number of bed"]==0) & (df["Price"]>2000),"Number of bed"] = 3
+
+    df["Rating"] = df["Rating"].apply(lambda x: np.nan if x==0 else x)
+    df["Rating"].fillna(np.round(df["Rating"].mean(), 1), inplace=True)
+
+    scaler = get_scaler(df)
+
+
+    col1, col2 = st.columns([0.3,0.7], gap="large")
+
+    with col1:
+        # Collect user preferences
+        st.markdown(" ")
+        st.markdown(" ")
+        st.markdown("")
+        #st.markdown("#### Filter preferences")
+        list_countries = df["Country"].unique()
+        location = st.selectbox("Select a Country",list_countries, index=0)
+
+        list_nb_beds = df["Number of bed"].unique()
+        num_beds = st.selectbox("Number of beds", list_nb_beds, index=0)
+        #if num_beds == "No information"
+
+        min_rating, max_rating = st.slider("Range of ratings", min_value=df["Rating"].min(), max_value=df["Rating"].max(), step=0.1, value=(5.0, df["Rating"].max()))
+        min_price, max_price = st.slider("Range of room prices", min_value=df["Price"].min(), max_value=df["Price"].max(), step=10, value=(df["Price"].min(), 10000))
+
+        # Convert price range sliders to integer values
+        min_price = int(min_price)
+        max_price = int(max_price)
+
+    with col2:
+        country_info(location)
+
+
+    preferences = {
+        'Location': location,
+        'Number of beds': num_beds,
+        'Rating': [min_rating, max_rating],
+        'Price': [min_price, max_price],
+    }
+            
+
+    if st.button("Recommend Hotels"):
+        st.info("Hotels were recommended based on how similar they were to the users preferences.")
+        
+        # Default number of recommendations to show
+        max_recommendations = 5
+        
+        # Call the recommendation function
+        recommended_hotels, message = recommend_hotels_with_location_and_beds(df, preferences, max_recommendations)
+        
+        # If no recommendations, reduce the maximum number of recommendations and try again
+        if recommended_hotels.empty:
+            max_recommendations -= 1
+            recommended_hotels, message = recommend_hotels_with_location_and_beds(df, preferences, max_recommendations)
+            if recommended_hotels.empty:
+                st.error(message)
+            # else:
+            #     st.write(recommended_hotels)
+        else:
+            st.markdown(" ")
+            for i in range(len(recommended_hotels)):
+                #st.dataframe(recommended_hotels)
+                df_result = recommended_hotels.iloc[i,:]                    
+                col1_, col2_ = st.columns([0.4,0.6], gap="medium")
+
+                with col1_:
+                    st.image("images/room.jpg",width=100)
+                    st.markdown(f"### {i+1}: {df_result['Hotel Name']}")
+                    st.markdown(f"""**{df_result['Room Type']}** <br>
+                                with {df_result['Bed Type']}
+                                """, unsafe_allow_html=True)
+                with col2_:
+                    st.markdown(" ")
+                    st.markdown(" ")
+                    annotated_text("**Number of beds :** ",(f"{df_result['Number of bed']}","","#faa"))
+                    #st.markdown(f"**Bed type**: {df_result['Bed Type']}")
+                    annotated_text("**City:** ",(f"{df_result['City']}","","#afa"))
+                    annotated_text("**Rating:** ",(f"{df_result['Rating']}","","#8ef"))
+                    annotated_text("**Price:** ",(f"{df_result['Price']}$","","#fea"))
+                
+                st.divider()
+                    
\ No newline at end of file
diff --git a/pages/sentiment_analysis.py b/pages/sentiment_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c2fb464960eed27c8a56e5f056477f10e163d5c
--- /dev/null
+++ b/pages/sentiment_analysis.py
@@ -0,0 +1,256 @@
+import os
+import re
+import time
+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import altair as alt
+import plotly.express as px
+
+from pysentimiento import create_analyzer
+from utils import load_data_pickle
+
+st.set_page_config(layout="wide")
+
+def clean_text(text):
+    pattern_punct = r"[^\w\s.',:/]"
+    pattern_date = r'\b\d{1,2}/\d{1,2}/\d{2,4}\b'
+
+    text = text.lower()
+    text = re.sub(pattern_date, '', text)
+    text = re.sub(pattern_punct, '', text)
+    text = text.replace("ggg","g")
+    text = text.replace("  "," ")
+    return text
+
+@st.cache_data(ttl=3600, show_spinner=False)
+def load_sa_model():
+    return create_analyzer(task="sentiment", lang="en")
+
+
+
+
+st.markdown("# Sentiment Analysis")
+
+st.markdown("### What is Sentiment Analysis ?")
+
+st.info("""
+    Sentiment analysis is a **Natural Language Processing** (NLP) task that involves determining the sentiment or emotion expressed in a piece of text. 
+    It has a wide range of use cases across various industries, as it helps organizations gain insights into the opinions, emotions, and attitudes expressed in text data.""")
+
+st.markdown("Here is an example of Sentiment analysis used to analyze **Customer Satisfaction** for perfums.")
+
+_, col, _ = st.columns([0.1,0.8,0.1])
+with col:
+    st.image("images/sentiment_analysis.png") #, width=800)
+
+st.markdown(" ")
+
+st.markdown("""
+Common applications of Natural Language Processing include:
+- **Customer Feedback and Reviews** 💯: Assessing reviews on products or services to understand customer satisfaction and identify areas for improvement.
+- **Market Research** 🔍: Analyzing survey responses or online forums to gauge public opinion on products, services, or emerging trends.
+- **Financial Market Analysis** 📉: Monitoring financial news, reports, and social media to gauge investor sentiment and predict market trends.
+- **Government and Public Policy** 📣: Analyzing public opinion on government policies, initiatives, and political decisions to gauge public sentiment and inform decision-making.        
+""")
+
+st.divider()
+
+#sa_pages = ["Starbucks Customer Reviews (Text)", "Tiktok's US Congressional Hearing (Audio)"]
+#st.markdown("### Select a use case ")
+#use_case = st.selectbox("", sa_pages, label_visibility="collapsed")
+
+
+st.markdown("### Customer Reviews 📝")
+st.info("""In this use case, **sentiment analysis** is used to predict the **polarity** (negative, neutral, positive) of customer reviews. 
+           You can try the application by using the provided starbucks customer reviews, or by writing your own.""") 
+st.markdown(" ")
+
+_, col, _ = st.columns([0.25,0.5,0.25])
+with col:
+    st.image("images/reviews.jpg")
+
+st.markdown(" ")
+
+
+# Load data 
+path_sa = "data/sa_data"
+reviews_df = load_data_pickle(path_sa,"reviews_raw.pkl")
+reviews_df.reset_index(drop=True, inplace=True)
+reviews_df["Date"] = reviews_df["Date"].dt.date
+reviews_df["Year"] = reviews_df["Year"].astype(int)
+
+
+
+st.markdown("#### Predict polarity 🤔")
+tab1_, tab2_ = st.tabs(["Starbucks reviews", "Write a review"])
+
+with tab1_:
+    # FILTER DATA
+    st.markdown(" ")
+
+    col1, col2 = st.columns([0.2, 0.8], gap="medium")
+
+    with col1:
+        st.markdown("""<b>Filter reviews: </b> <br>
+                    You can filter the dataset by Date, State or Rating""", unsafe_allow_html=True)
+        
+        select_image_box = st.radio("",
+        ["Filter by Date (Year)", "Filter by State", "Filter by Rating", "No filters"],
+        index=3, label_visibility="collapsed")
+
+        if select_image_box == "Filter by Date (Year)":
+            selected_date = st.multiselect("Date (Year)", reviews_df["Year"].unique(), default=reviews_df["Year"].unique()[0])
+            reviews_df = reviews_df.loc[reviews_df["Year"].isin(selected_date)]
+
+        if select_image_box == "Filter by State":
+            selected_state = st.multiselect("State", reviews_df["State"].unique(), default=reviews_df["State"].unique()[0])
+            reviews_df = reviews_df.loc[reviews_df["State"].isin(selected_state)]
+
+        if select_image_box == "Filter by Rating":
+            selected_rating = st.multiselect("Rating", sorted(list(reviews_df["Rating"].dropna().unique())), 
+                                             default = sorted(list(reviews_df["Rating"].dropna().unique()))[0])
+            reviews_df = reviews_df.loc[reviews_df["Rating"].isin(selected_rating)]
+
+        if select_image_box == "No filters":
+            pass
+
+        #st.slider()
+        run_model1 = st.button("**Run the model**", type="primary", key="tab1")
+        st.info("The model has already been trained in this use case.")
+
+    with col2:
+    # VIEW DATA
+        st.markdown("""<b>View the reviews:</b> <br>
+                    The dataset contains the location (State), date, rating, text and images (if provided) for each review.""", 
+                    unsafe_allow_html=True)
+        
+        st.data_editor(
+            reviews_df.drop(columns=["Year"]), 
+            column_config={"Image 1": st.column_config.ImageColumn("Image 1"), 
+                            "Image 2": st.column_config.ImageColumn("Image 2")},
+            hide_index=True)
+        
+
+######### SHOW RESULTS ########
+    if run_model1:
+        with st.spinner('Wait for it...'):
+            df_results = load_data_pickle(path_sa,"reviews_results.pkl")
+            df_results.reset_index(drop=True, inplace=True)
+
+            index_row = np.array(reviews_df.index)
+            df_results = df_results.iloc[index_row].reset_index(drop=True)
+            df_results["Review"] = reviews_df["Review"]
+            st.markdown("  ")
+
+            st.markdown("#### See the results ☑️")
+            tab1, tab2, tab3 = st.tabs(["All results", "Results per state", "Results per year"])        
+            
+            with tab1: # Overall results (tab_1)
+                # get results df
+                df_results_tab1 = df_results[["ID","Review","Rating","Negative","Neutral","Positive","Result"]]
+
+                # warning message
+                df_warning = df_results_tab1["Result"].value_counts().to_frame().reset_index()
+                df_warning["Percentage"] = (100*df_warning["count"]/df_warning["count"].sum()).round(2)
+                
+                perct_negative = df_warning.loc[df_warning["Result"]=="Negative","Percentage"].to_numpy()[0]
+                if perct_negative > 50:
+                    st.error(f"**Negative reviews alert** ⚠️: The proportion of negative reviews is {perct_negative}% !")
+
+                # show dataframe results
+                st.data_editor(
+                    df_results_tab1, #.loc[df_results_tab1["Customer ID"].isin(filter_customers)],
+                    column_config={
+                        "Negative": st.column_config.ProgressColumn(
+                            "Negative 👎",
+                            help="Negative score of the review",
+                            format="%d%%",
+                            min_value=0,
+                            max_value=100),
+                        "Neutral": st.column_config.ProgressColumn(
+                            "Neutral ✋",
+                            help="Neutral score of the review",
+                            format="%d%%",
+                            min_value=0,
+                            max_value=100),
+                        "Positive": st.column_config.ProgressColumn(
+                            "Positive 👍",
+                            help="Positive score of the review",
+                            format="%d%%",
+                            min_value=0,
+                            max_value=100)},
+                        hide_index=True,
+                )
+
+            with tab2: # Results by state (tab_1)
+                avg_state = df_results[["State","Negative","Neutral","Positive"]].groupby(["State"]).mean().round()
+                avg_state = avg_state.reset_index().melt(id_vars="State", var_name="Sentiment", value_name="Score (%)")
+
+                chart_state = alt.Chart(avg_state, title="Review polarity per state").mark_bar().encode(
+                    x=alt.X('Sentiment', axis=alt.Axis(title=None, labels=False, ticks=False)),
+                    y=alt.Y('Score (%)', axis=alt.Axis(grid=False)),
+                    color='Sentiment',
+                    column=alt.Column('State', header=alt.Header(title=None, labelOrient='bottom'))
+                ).configure_view(
+                    stroke='transparent'
+                ).interactive()
+
+                st.markdown(" ")
+                st.altair_chart(chart_state)
+
+
+            with tab3: # Results by year (tab_1)
+                avg_year = df_results[["Year","Negative","Neutral","Positive"]]
+                #avg_year["Year"] = avg_year["Year"].astype(str)
+                avg_year = avg_year.groupby(["Year"]).mean().round()
+                avg_year = avg_year.reset_index().melt(id_vars="Year", var_name="Sentiment", value_name="Score (%)")
+
+                chart_year = alt.Chart(avg_year, title="Evolution of review polarity").mark_area(opacity=0.5).encode(
+                    x='Year',
+                    y='Score (%)',
+                    color='Sentiment',
+                ).interactive()
+
+                st.markdown(" ")
+                st.altair_chart(chart_year, use_container_width=True)
+        
+        # else:
+        #     st.warning("You must select at least one review to run the model.")
+
+
+#### WRITE YOUR OWN REVIEW #####""
+with tab2_:
+    st.markdown("**Write your own review**")
+
+    txt_review = st.text_area(
+        "Write your review",
+        "I recently visited a local Starbucks, and unfortunately, my experience was far from satisfactory. "
+        "From the moment I stepped in, the atmosphere felt chaotic and disorganized. "
+        "The staff appeared overwhelmed, leading to a significant delay in receiving my order. "
+        "The quality of my drink further added to my disappointment. " 
+        "The coffee tasted burnt, as if it had been sitting on the burner for far too long.",
+        label_visibility="collapsed"
+        )
+    
+    run_model2 = st.button("**Run the model**", type="primary", key="tab2")
+        
+    if run_model2:
+        with st.spinner('Wait for it...'):
+            #sentiment_analyzer = create_analyzer(task="sentiment", lang="en")
+            # Load model with cache
+            sentiment_analyzer = load_sa_model()
+            q = sentiment_analyzer.predict(txt_review)
+
+            df_review_user = pd.DataFrame({"Polarity":["Positive","Neutral","Negative"], 
+                        "Score":[q.probas['POS'], q.probas['NEU'], q.probas['NEG']]})
+            
+            st.markdown(" ")
+            st.info(f"""Your review was **{int(q.probas['POS']*100)}%** positive, **{int(q.probas['NEU']*100)}%** neutral 
+                    and **{int(q.probas['NEG']*100)}%** negative.""")
+
+            fig = px.bar(df_review_user, x='Score', y='Polarity', color="Polarity", title='Sentiment analysis results', orientation="h")
+            st.plotly_chart(fig, use_container_width=True)
+
+        
\ No newline at end of file
diff --git a/pages/supervised_unsupervised_page.py b/pages/supervised_unsupervised_page.py
new file mode 100644
index 0000000000000000000000000000000000000000..652a3e49ef68791795f989ffafec53819aee7fdf
--- /dev/null
+++ b/pages/supervised_unsupervised_page.py
@@ -0,0 +1,478 @@
+import os
+import time
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+from utils import load_data_pickle, load_model_pickle
+from annotated_text import annotated_text
+
+#####################################################################################
+#                                    PAGE CONFIG
+#####################################################################################
+
+st.set_page_config(layout="wide")
+
+
+
+#####################################################################################
+#                                    INTRO 
+#####################################################################################
+
+
+st.markdown("# Supervised vs Unsupervised Learning")
+
+st.info("""There are two main types of models in the field of Data Science, **Supervised** and **Unsupervised learning** models. 
+        Being able to distinguish which type of model fits your data is an essential step in building any AI project.""")
+
+st.markdown(" ")
+#st.markdown("## What are the differences between both ?")
+
+col1, col2 = st.columns(2, gap="large")
+
+with col1: 
+    st.markdown("### Supervised Learning")
+    st.markdown("""In supervised learning, models are trained by learning from **labeled data**. <br>
+                Labeled data provides to the model the desired output, which it will then use to learn relevant patterns and make predictions. 
+- A model is first **trained** to make predictions using labeled data
+- The trained model can then be used to **predict values** for new data.
+                """, unsafe_allow_html=True)
+    st.markdown(" ")
+    st.image("images/supervised_learner.png", caption="An example of supervised learning")
+
+with col2:
+    st.markdown("### Unsupervised Learning")
+    st.markdown("""In unsupervised learning, models **learn the data's inherent structure** without any explicit guidance on what to look for.
+                The algorithm will identify any naturally occurring patterns in the dataset using **unlabeled data**. 
+- They can be useful for applications where the goal is to discover **unknown groupings** in the data.
+- They are also used to identify unusual patterns or **outliers**.
+                """, unsafe_allow_html=True)
+    st.markdown(" ")
+    st.image("images/unsupervised_learner.webp", caption="An example of unsupervised Learning")
+
+st.markdown("  ")
+
+learning_type = st.selectbox("**Select a type of model**", 
+                             ["Supervised Learning", 
+                           "Unsupervised Learning"])
+
+
+
+
+
+#######################################################################################################################
+#                                               SUPERVISED LEARNING
+#######################################################################################################################
+
+
+if learning_type == "Supervised Learning":
+    sl_usecase = st.selectbox("**Choose a use case**", 
+                          ["Credit score classification 💯", 
+                           "Customer churn prediction ❌"])
+    
+    st.markdown(" ")
+    
+    # st.divider()
+    
+    path_data_supervised = r"data/classification"
+    path_pretrained_supervised = r"pretrained_models/supervised_learning"
+    
+    ################################# CREDIT SCORE ######################################
+    
+    if sl_usecase == "Credit score classification 💯":
+
+        path_credit = os.path.join(path_data_supervised,"credit_score")
+        
+        ## Description of the use case
+        st.divider()
+        st.markdown("## Credit score classification 💯")
+        st.info("""**Classification** is a type of supervised learning where the goal is to categorize input data into predefined classes or categories. 
+                In this case, we will build a **credit score classification** model that predicts if a client will have a **'Bad'**, **'Standard'** or **'Good'** credit score.""")
+        st.markdown(" ")
+
+        _, col, _ = st.columns([0.25,0.5,0.25])
+        with col:
+            st.image("images/credit_score.jpg")
+
+        ## Learn about the data
+        st.markdown("#### About the data 📋")
+        st.markdown("""To train the credit classification model, you were provided a **labeled** database with the bank and credit-related information of around 7600 clients. <br> 
+                    This dataset is 'labeled' since it contains information on what we are trying to predict, which is the **Credit_Score** variable.""", 
+                    unsafe_allow_html=True)
+        
+        ## Load data 
+        credit_train = load_data_pickle(path_credit, "credit_score_train_raw.pkl")
+        credit_test_pp = load_data_pickle(path_credit, "credit_score_test_pp.pkl")
+        labels = ["Good","Poor","Standard"]
+
+        ## Load model
+        credit_model = load_model_pickle(path_pretrained_supervised,"credit_score_model.pkl")
+
+        # View data
+        see_data = st.checkbox('**See the data**', key="credit_score\data")
+        if see_data:
+            st.warning("The data of only the first 30 clients are shown.") 
+            st.dataframe(credit_train.head(30).reset_index(drop=True))
+
+        learn_data = st.checkbox('**Learn more about the data**', key="credit_score_var")
+        if learn_data:
+            st.markdown("""
+- **Age**: The client's age
+- **Occupation**: The client's occupation/job
+- **Credit_Mix**: Score for the different type of credit accounts a client has (mortgages, loans, credit cards, ...)
+- **Payment_of_Min_Amount**: Whether the client is making the minimum required payments on their credit accounts (Yes, No, NM:Not mentioned)
+- **Annual_Income**: The client's annual income
+- **Num_Bank_Accounts**: Number of bank accounts opened 
+- **Num_Credit_Card**: Number of credit cards owned
+- **Interest_Rate**: The client's average interest rate 
+- **Num_of_Loan**: Number of loans of the client
+- **Changed_Credit_Limit**: Whether a client changed his credit limit once or not (Yes, No)                 - 
+- **Outstanding Debt**: A client's outstanding debt
+- **Credit_History_Age**: The length of a client's credit history (in months)
+""")
+            
+        st.markdown("  ")
+        st.markdown("  ")
+
+        ## Train the algorithm
+        st.markdown("#### Train the algorithm ⚙️")
+        st.info("""**Training** an AI model means feeding it data that contains multiple examples of clients with their credit scores. 
+                Using the labeled data provided, the model will **learn relationships** between a client's credit score and the other bank/credit-related variables provided.
+                Using these learned relationships, the model will then try to make **accurate predictions**.""")
+
+        # st.markdown("""Before feeding the model data for training, exploratory data analysis is often conducted to discover if patterns can discovered beforehand.""")            
+        # st.image("images/models/credit_score/EDA_numeric_credit.png")
+        #st.markdown("In our case, the training data is the dataset containing the bank and credit information of our 7600 customers.")
+
+        if 'model_train' not in st.session_state:
+            st.session_state['model_train'] = False
+
+        if st.session_state.model_train:
+            st.write("The model has been trained.")
+        else:
+            st.write("The model hasn't been trained yet")
+
+        run_credit_model = st.button("**Train the model**")
+
+        if run_credit_model:
+            st.session_state.model_train = True
+            with st.spinner('Wait for it...'):
+                st.markdown(" ")
+                st.markdown(" ")
+                time.sleep(2)
+                st.markdown("#### See the results ☑️")
+                tab1, tab2 = st.tabs(["Performance", "Explainability"])
+                
+                ######## MODEL PERFORMANCE 
+                with tab1:
+                    results_train = load_data_pickle(path_credit,"credit_score_cm_train")
+                    results_train = results_train.to_numpy()
+                    accuracy = np.round(results_train.diagonal()*100)
+                    df_accuracy = pd.DataFrame({"Credit Score":["Good","Poor","Standard"],
+                                                "Accuracy":accuracy})
+
+                    st.markdown(" ")
+                    st.info("""**Evaluating a model's performance** helps provide a quantitative measure of the model's ability to make accurate decisions.
+                                In this use case, the performance of the credit score model was measured by comparing clients' true credit scores with the scores predicted by the trained model.""")
+
+                    fig = px.bar(df_accuracy, y='Accuracy', x='Credit Score', color="Credit Score", title="Model performance")
+                    st.plotly_chart(fig, use_container_width=True)
+
+                    st.markdown("""<i>The model's accuracy was measured for every type of credit score (Good, Standard, Poor).</i>
+                                <i>This is crucial as to understand whether the model is consistant in its performance, or whether it has trouble distinguishing between two kinds of credit score.</i>""", 
+                                unsafe_allow_html=True)
+
+                    st.markdown(" ")
+
+                    st.markdown("""**Interpretation**: <br>
+                                Our model's is overall quite accurate in predicting all types of credit scores with an accuracy that is above 85% for each. 
+                                We do note that is slighly more accuracte in predicting a good credit score (92%) and less for a standard credit score (86%). 
+                                This can be due to the model having a harder time distinguishing between clients with a standard credit score and other more "extreme" credit scores (Good, Bad).
+                                """, unsafe_allow_html=True)
+                    
+                ##### MODEL EXPLAINABILITY
+                with tab2:
+                    st.markdown(" ")
+                    st.info("""**Explainability** in AI refers to the ability to understand which variable used by a model during training had the most impact on the final predictions and how to quantify this impact.
+                            Understanding the inner workings of a model helps build trust among users and stakeholders, as well as increase acceptance.""")
+                    
+                    # Create feature importance dataframe
+                    df_var_importance = pd.DataFrame({"variable":credit_test_pp.columns,
+                                                    "score":credit_model.feature_importances_})
+                    
+                    # Compute average score for categorical variables
+                    for column in ["Occupation","Credit_Mix","Payment_of_Min_Amount"]:
+                        col_remove = [col for col in credit_test_pp.columns if f"{column}_" in col]
+                        avg_score = df_var_importance.loc[df_var_importance["variable"].isin(col_remove)]["score"].mean()
+                        
+                        df_var_importance = df_var_importance.loc[~df_var_importance["variable"].isin(col_remove)]                    
+                        new_row = pd.DataFrame([[column, avg_score]], columns=["variable","score"])
+                        df_var_importance = pd.concat([df_var_importance, new_row], ignore_index=True)
+                    
+                    df_var_importance.sort_values(by=["score"], inplace=True)
+                    df_var_importance["score"] = df_var_importance["score"].round(3)                
+
+                    # Feature importance plot with plotly
+                    fig = px.bar(df_var_importance, x='score', y='variable', color="score", orientation="h", title="Model explainability")
+                    st.plotly_chart(fig, use_container_width=True)
+
+                    st.markdown("""<b>Interpretation</b>: <br>
+                                A client's outstanding debt, interest rate and delay from due date were the most crucial factors in explaining their final credit score. <br>
+                                Whether a client is making their minimum required payments on their credit accounts (Payment_min_amount), their occupation and their number of loans had a very limited impact on their credit score.,
+                                """, unsafe_allow_html=True)
+                    
+
+        st.markdown(" ")
+        st.markdown(" ")
+        st.markdown("#### Predict credit score 🆕")
+        st.info("You can only predict the credit score of new clients once the **model has been trained.**")
+        st.markdown("  ")
+        
+        col1, col2 = st.columns([0.25,0.75], gap="medium")
+        
+        credit_test = load_data_pickle(path_credit,"credit_score_test_raw.pkl")
+        credit_test.reset_index(drop=True, inplace=True)
+        credit_test.insert(0, "Client ID", [f"{i}" for i in range(credit_test.shape[0])])
+        credit_test = credit_test.loc[credit_test["Num_Bank_Accounts"]>0]
+        #credit_test.drop(columns=["Credit_Score"], inplace=True)
+        
+        with col1:
+            st.markdown("""<b>Filter the data</b> <br>
+            You can select clients based on their *Age*, *Annual income* or *Oustanding Debt*.""", 
+            unsafe_allow_html=True)
+
+            select_image_box = st.radio(" ",
+            ["Filter by Age", "Filter by Income", "Filter by Outstanding Debt", "No filters"],
+            label_visibility="collapsed")
+
+            if select_image_box == "Filter by Age":
+                st.markdown(" ")
+                min_age, max_age = st.slider('Select a range', credit_test["Age"].astype(int).min(), credit_test["Age"].astype(int).max(), (19,50), 
+                                             key="age", label_visibility="collapsed")
+                credit_test = credit_test.loc[credit_test["Age"].between(min_age,max_age)]
+
+            if select_image_box == "Filter by Income":
+                st.markdown(" ")
+                min_income, max_income = st.slider('Select a range', credit_test["Annual_Income"].astype(int).min(), 180000, 
+                                                   (7000, 100000), label_visibility="collapsed", key="income")
+                credit_test = credit_test.loc[credit_test["Annual_Income"].between(min_income, max_income)]
+
+            if select_image_box == "Filter by Outstanding Debt":
+                min_debt, max_debt = st.slider('Select a range', credit_test["Outstanding_Debt"].astype(int).min(), credit_test["Outstanding_Debt"].astype(int).max(), 
+                                                (0,2000), label_visibility="collapsed", key="debt")
+                credit_test = credit_test.loc[credit_test["Outstanding_Debt"].between(min_debt, max_debt)]
+
+            if select_image_box == "No filters":
+                pass
+
+        st.markdown(" ")
+        st.markdown("""<b>Select a threshold for the alert</b> <br>
+                    A warning message will be displayed if the percentage of poor credit scores exceeds this threshold.
+                    """, unsafe_allow_html=True)
+        warning_threshold = st.slider('Select a value', min_value=20, max_value=100, step=10, 
+                                        label_visibility="collapsed", key="warning")
+
+        st.markdown(" ")
+        st.write("The threshold is at", warning_threshold, "%")
+            
+
+        with col2:
+            #st.markdown("**View the database**")
+            st.dataframe(credit_test)
+        
+        make_predictions = st.button("**Make predictions**")
+        st.markdown(" ")
+
+        if make_predictions:
+            if st.session_state.model_train is True:
+                X_test = credit_test_pp.iloc[credit_test.index,:]
+                predictions = credit_model.predict(X_test)
+                
+                df_results_pred = credit_test.copy()
+                df_results_pred["Credit Score"] = predictions
+                df_mean_pred = df_results_pred["Credit Score"].value_counts().to_frame().reset_index()
+                df_mean_pred.columns = ["Credit Score", "Proportion"]
+                df_mean_pred["Proportion"] = (100*df_mean_pred["Proportion"]/df_results_pred.shape[0]).round()
+                
+                perct_bad_score = df_mean_pred.loc[df_mean_pred["Credit Score"]=="Poor"]["Proportion"].to_numpy()
+
+                if perct_bad_score >= warning_threshold:
+                    st.error(f"The proportion of clients with a bad credit score is above {warning_threshold}% (at {perct_bad_score[0]}%)⚠️")
+
+                col1, col2 = st.columns([0.4,0.6], gap="large")
+                with col1:
+                    st.markdown("**Proporition of predicted credit scores**")
+                    fig = px.pie(df_mean_pred, values='Proportion', names='Credit Score')
+                                        #title="Proportion of credit scores")
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                with col2:
+                    df_show_results = df_results_pred[["Credit Score","Client ID"] + [col for col in df_results_pred.columns if col not in ["Client ID","Credit Score"]]]                    
+                    columns_float = df_show_results.select_dtypes(include="float").columns
+                    df_show_results[columns_float] = df_show_results[columns_float].astype(int)
+                    
+                    def highlight_score(val):
+                        if val == "Good":
+                            color = 'red'
+                        if val == 'Standard':
+                            color= "cornflowerblue"
+                        if val == "Poor":
+                            color = 'blue'
+                        return f'color: {color}'
+                    
+                    df_show_results_color = df_show_results.style.applymap(highlight_score, subset=['Credit Score'])
+                    
+                    st.markdown("**Overall results**")
+                    st.dataframe(df_show_results_color)
+
+            else:
+                st.error("You have to train the credit score model first.")
+
+
+
+
+    ################################# CUSTOMER CHURN #####################################
+        
+    elif sl_usecase == "Customer churn prediction ❌":
+        st.warning("This page is under construction")
+
+
+
+
+
+#######################################################################################################################
+#                                           UNSUPERVISED LEARNING
+#######################################################################################################################
+
+
+def markdown_general_info(df):
+    text = st.markdown(f"""
+- **Age**: {int(np.round(df.Age))}
+- **Yearly income**: {int(df.Income)} $
+- **Number of kids**: {df.Kids}
+- **Days of enrollment**: {int(np.round(df.Days_subscription))}
+- **Web visits per month**: {df.WebVisitsMonth}    
+""")   
+    return text
+
+
+
+if learning_type == "Unsupervised Learning":
+    usl_usecase = st.selectbox("**Choose a use case**", 
+                          ["Customer segmentation 🧑‍🤝‍🧑"])
+    
+
+    #################################### CUSTOMER SEGMENTATION ##################################
+
+    path_clustering = r"data/clustering"
+    path_clustering_results = r"data/clustering/results"
+
+    if usl_usecase == "Customer segmentation 🧑‍🤝‍🧑":
+
+        # st.divider()
+        st.divider()
+        st.markdown("## Customer Segmentation (Clustering) 🧑‍🤝‍🧑")
+
+        st.info("""**Unsupervised learning** methods, such as clustering, are valulable tools for cases where you want a model to discover patterns by itself, without having to give it examples to learn from.
+                    They can be useful for companies that want to perform **Customer Segmentation**. 
+                    The AI clustering model can identify unknown groups of clients, which in turn helps the company create more targeted add campaigns, based on their consumer's behavior and preferences.
+        """)
+        st.markdown("  ")
+
+        ## Show image
+        _, col, _ = st.columns([0.2,0.5,0.3])
+        with col:
+            st.image("images/cs.webp")
+
+        ## About the use case
+        st.markdown("#### About the use case 📋")
+        st.markdown("""You are giving a database that contains information on around 2000 customers of a mass-market retailer. 
+                    The database's contains personal information (age, income, number of kids...), as well as information on the client's behavior. 
+                    This includes what types of products were purchased by the client, how long has he been enrolled as a client and where these purchases were made. """, unsafe_allow_html=True)
+
+        see_data = st.checkbox('**See the data**', key="dataframe")
+
+        if see_data:
+            customer_data = load_data_pickle(path_clustering, "clean_marketing.pkl") 
+            st.dataframe(customer_data.head(10))
+
+        learn_data = st.checkbox('**Learn more about the variables**', key="variable")
+
+        if learn_data:
+            st.markdown("""
+        - **Age**: Customer's age
+        - **Income**: Customer's yearly household income
+        - **Kids**: Number of children/teenagers in customer's household
+        - **Days_subscription**: Number of days since a customer's enrollment with the company
+        - **Recency**: Number of days since customer's last purchase
+        - **Wines**: Proportion of money spent on wine in last 2 years
+        - **Fruits**: Proportion of money spent on fruits in last 2 years
+        - **MeatProducts**: Proportion of money spent on meat in last 2 years
+        - **FishProducts**: Proportion of money spent on fish in last 2 years
+        - **SweetProducts**: Proportion of money spent sweets in last 2 years
+        - **DealsPurchases**: Proportion of purchases made with a discount
+        - **WebPurchases**: Proportion of purchases made through the company’s website
+        - **CatalogPurchases**: Proporition of purchases made using a catalogue
+        - **StorePurchases**: Proportion of purchases made directly in stores
+        - **WebVisitsMonth**: Proportion of visits to company’s website in the last month""")
+            st.divider()
+
+
+        st.markdown(" ")
+        st.markdown(" ")
+
+        st.markdown("#### Clustering algorithm ⚙️")
+
+        st.info("""**Clustering** is a type of unsupervised learning method that learns how to group similar data points together into "clusters", without needing supervision. 
+                    In our case, a data points represents a customer that will be assigned to an unknown group.""")
+        
+        st.markdown(""" 
+- The clustering algorithm used in this use case allows a specific number of groups to be identified, which isn't the case for all clustering models.
+- The number of clusters chosen by the user can have a strong impact on the quality of the segmentation. Try to run the model multiple times with different number of clusters and see which number leads to groups with more distinct customer behaviors/preferences.""")
+        st.markdown(" ")
+        st.markdown("Here is an example of grouped data using a clustering model.")
+        st.image("images/clustering.webp")
+
+
+        nb_groups = st.selectbox("Choose a number of customer groups to identify", np.arange(2,6))
+        df_results = load_data_pickle(path_clustering_results, f"results_{nb_groups}_clusters.pkl")
+
+        st.markdown("  ")
+        run_model = st.button("**Run the model**")
+        #tab1, tab2 = st.tabs(["Results per product type", "Results per channel"])
+        #st.divider()
+
+        if run_model:
+            cols_group = st.columns(int(nb_groups))
+            for nb in range(nb_groups):
+                df_nb = df_results[nb]
+
+                col1, col2 = st.columns([0.3,0.7])
+                with col1:
+                    st.image("images/group.png", width=200)
+                    st.header(f"Group {nb+1}", divider="grey")
+                    markdown_general_info(df_nb)
+
+                with col2:
+                    tab1, tab2 = st.tabs(["Results per product type", "Results per channel"])
+                    list_product_col = [col for col in list(df_nb.index) if "Products" in col]
+                    df_products = df_nb.reset_index()
+                    df_products = df_products.loc[df_products["variable"].isin(list_product_col)]
+                    df_products.columns = ["variables", "values"]
+                    
+                    with tab1:
+                        fig = px.pie(df_products, values='values', names='variables', 
+                                        title="Amount spent per product type (in %)")
+                        st.plotly_chart(fig, width=300)
+
+                    list_purchases_col = [col for col in list(df_nb.index) if "Purchases" in col]
+                    df_products = df_nb.reset_index()
+                    df_products = df_products.loc[df_products["variable"].isin(list_purchases_col)]
+                    df_products.columns = ["variables", "values"]
+                    
+                    with tab2:
+                        fig = px.pie(df_products, values='values', names='variables', 
+                                    title='Proportion of purchases made per channel (in %)')
+                        st.plotly_chart(fig, width=300)
+        
diff --git a/pages/timeseries_analysis.py b/pages/timeseries_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..920aae44d552af3dd0c26ea89af0fc37db60d397
--- /dev/null
+++ b/pages/timeseries_analysis.py
@@ -0,0 +1,314 @@
+import streamlit as st
+import pandas as pd
+import altair as alt
+import plotly.express as px
+import numpy as np
+
+from PIL import Image
+from prophet import Prophet
+from datetime import date
+from utils import load_data_pickle
+from sklearn.metrics import root_mean_squared_error
+
+
+st.set_page_config(layout="wide")
+
+@st.cache_data(ttl=3600, show_spinner=False)
+def forecast_prophet(train, test, col=None):
+    model = Prophet(daily_seasonality=False)
+    
+    for col in select_add_var:
+        model.add_regressor(col)
+    
+    model.fit(train)
+    forecast = model.predict(test)
+    return model, forecast
+
+
+###################################### TITLE ####################################
+
+#st.image("images/ts_header.png")
+st.markdown("# Time Series Forecasting")
+
+st.markdown("### What is Time Series Forecasting ?")
+st.info("""Time series forecasting models are AI models built to make accurate predictions about future values using historical data. 
+            These types of models take into account temporal patterns, such as **trends** (long-term movements), **seasonality** (repeating patterns at fixed intervals), and **cyclic patterns** (repeating patterns not necessarily at fixed intervals)""")
+            #unsafe_allow_html=True)
+
+st.markdown(" ")
+image_ts = Image.open('images/ts_patterns.png')
+_, col, _ = st.columns([0.15,0.7,0.15])
+with col:
+    st.image(image_ts)
+
+st.markdown("    ")
+
+st.markdown("""Real-life applications of time series forecasting include:
+- **Finance 💰**: Predict stock prices based on historical data to assist investors and traders in making informed decisions.
+- **Energy ⚡**: Forecast energy consumption patterns to optimize resource allocation, plan maintenance, and manage energy grids more efficiently.
+- **Retail 🏬**: Predict future demand for products to optimize inventory levels, reduce holding costs, and improve supply chain efficiency.
+- **Transportation and Traffic flow :car:**: Forecasting traffic patterns to optimize route planning, reduce congestion, and improve overall transportation efficiency.
+- **Healthcare** 👨‍⚕️: Predicting the number of patient admissions to hospitals, helping healthcare providers allocate resources effectively and manage staffing levels.
+- **Weather 🌦️**: Predicting weather conditions over time, which is crucial for planning various activities, agricultural decisions, and disaster preparedness.
+""")
+
+
+
+st.markdown("    ")
+
+
+
+
+###################################### USE CASE #######################################
+
+# LOAD DATASET
+path_timeseries = r"data/household"
+data_model = load_data_pickle(path_timeseries,"household_power_consumption_clean.pkl")
+data_model.rename({"Date":"ds", "Global_active_power":"y"}, axis=1, inplace=True)
+data_model.dropna(inplace=True)
+data_model["ds"] = pd.to_datetime(data_model["ds"])
+
+# BEGINNING OF USE CASE
+st.divider()
+st.markdown("### Power Consumption Forecasting ⚡")
+
+#st.markdown("  ")
+st.info("""In this use case, a time series forecasting model is used to predict the **energy consumption** (or **Global Active Power**) of a household using historical data. 
+        A forecasting model can be a valuable tool to optimize resource planning and avoid overloads during peak demand periods.""")
+
+st.markdown(" ")
+
+_, col, _ = st.columns([0.15,0.7,0.15])
+with col:
+    st.image("images/energy_consumption.jpg")
+
+st.markdown("    ")
+st.markdown("    ")
+
+st.markdown("#### About the data 📋")
+
+st.markdown("""You were provided data from the **daily energy consumption** of a household between January 2007 and November 2010 (46 months). <br>
+            The goal is to forecast the **Global active power** being produced daily by the household. 
+            Additional variables such as *Global Intensity* and three levels of *Sub-metering* are also available for the forecast.
+            """, unsafe_allow_html=True)
+
+st.markdown(" ")
+
+st.info("""The data has been split into "historical data" and "to be predicted". Since forecasting models are **supervised**, we will use the household's energy data from January 2007 to December 2009 as historical data to train the model.
+        We will then use the rest of the available data (starting January 2010) to test the performance of the model.""")
+
+select_cutoff_date = date(2010, 1, 1)
+select_cutoff_date = select_cutoff_date.strftime('%Y-%m-%d')
+
+# SELECT TRAIN/TEST SET
+train = data_model[data_model["ds"] <= select_cutoff_date]
+test = data_model[data_model["ds"] > select_cutoff_date]
+
+# PLOT TRAIN/TEST SET
+train_plot = train.copy()
+train_plot["split"] = ["historical data"]*len(train_plot)
+
+test_plot = test.copy()
+test_plot["split"] = ["to be predicted"]*len(test_plot)
+data_clean_plot = pd.concat([train_plot, test_plot]) # plot dataset
+
+st.markdown(" ")
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Global active power", "Sub metering 1", "Sub metering 2", "Sub metering 3", "Global Intensity"])
+
+with tab1:
+    ts_chart = alt.Chart(data_clean_plot).mark_line().encode(
+        x=alt.X('ds:T', axis=alt.Axis(format='%b %Y', tickCount=12), title="Date"),
+        y=alt.Y('y:Q', title="Global active power"),
+        color='split:N',
+    ).interactive()
+
+    st.markdown("**View Global active power** (to be forecasted)")
+    st.altair_chart(ts_chart, use_container_width=True)
+    st.success("""**Global active power** refers to the total real power consumed by electrical devices in the house (in kilowatts).""")
+    
+
+with tab2:
+    ts_chart = alt.Chart(data_clean_plot.loc[data_clean_plot["split"]=="historical data"]).mark_line().encode(
+                x=alt.X('ds:T', axis=alt.Axis(format='%b %Y', tickCount=12), title="Date"),
+                y=alt.Y('Sub_metering_1:Q', title="Sub metering 1"),
+                color=alt.Color('split:N')) #, scale=custom_color_scale))
+            
+    st.markdown("**View Sub-metering 1** (additional)")
+    st.altair_chart(ts_chart.interactive(), use_container_width=True)
+    st.success("**Sub-metering 1** is the total active power consumed by the kitchen in the house (in kilowatts).")
+
+
+
+with tab3:    
+    ts_chart = alt.Chart(data_clean_plot.loc[data_clean_plot["split"]=="historical data"]).mark_line().encode(
+        x=alt.X('ds:T', axis=alt.Axis(format='%b %Y', tickCount=12), title="Date"),
+        y=alt.Y('Sub_metering_2:Q', title="Sub metering 2"),
+        color=alt.Color('split:N')) #, scale=custom_color_scale))
+
+    st.markdown("**View Sub-metering 2** (additional)")
+    st.altair_chart(ts_chart.interactive(), use_container_width=True)
+    st.success("**Sub-metering 2** is the total active power consumed by the laundry room in the house (in kilowatts).")
+
+
+with tab4:        
+    ts_chart = alt.Chart(data_clean_plot.loc[data_clean_plot["split"]=="historical data"]).mark_line().encode(
+            x=alt.X('ds:T', axis=alt.Axis(format='%b %Y', tickCount=12), title="Date"),
+            y=alt.Y('Sub_metering_3:Q', title="Sub metering 3"),
+            color=alt.Color('split:N')) #scale=custom_color_scale))
+
+    st.markdown("**View Sub-metering 3** (additional)")
+    st.altair_chart(ts_chart.interactive(), use_container_width=True)
+    st.success("**Sub-metering 3** is the active power consumed by the electric water heater and air conditioner in the household (in kilowatts).")
+
+
+with tab5:
+    custom_color_scale = alt.Scale(range=['red', 'lightcoral'])
+    ts_chart = alt.Chart(data_clean_plot.loc[data_clean_plot["split"]=="historical data"]).mark_line().encode(
+        x=alt.X('ds:T', axis=alt.Axis(format='%b %Y', tickCount=12), title="Date"),
+        y=alt.Y('Global_intensity:Q', title="Global active power"),
+        color=alt.Color('split:N')) # scale=custom_color_scale))
+
+    st.markdown("**View Global intensity** (additional)")
+    st.altair_chart(ts_chart.interactive(), use_container_width=True)
+    st.success("**Global intensity** is the average current intensity delivered to the household (amps).")
+
+
+
+st.markdown(" ")
+st.markdown(" ")
+st.markdown("#### Forecast model 📈")
+st.markdown("""The forecasting model used in this use case allows **additional data** to be used for training. 
+        Try adding more data to the model as it can help improve its performance and accuracy.""")
+
+
+
+# ADD VARIABLES TO ANALYSIS
+add_var = ["Sub_metering_1", "Sub_metering_2", "Sub_metering_3","Global_intensity"]
+st.markdown("")
+select_add_var = st.multiselect("**Add variables to the model**", add_var)
+
+if 'model_train' not in st.session_state:
+    st.session_state['model_train'] = False
+    
+# if st.session_state.model_train:
+#     text = "The model has alerady been trained."
+# else:
+#     st.write("The model hasn't been trained yet")
+
+st.markdown("")
+run_model = st.button("**Run the model**")
+
+
+st.markdown("    ")
+st.markdown("    ")
+
+
+
+
+
+################################## SEE RESULTS ###############################
+
+if "saved_model" not in st.session_state:
+    st.session_state["saved_model"] = False
+
+
+if run_model:
+    with st.spinner('Wait for it...'):
+        fbmodel, forecast = forecast_prophet(train, test, col=select_add_var)
+        st.session_state.model_train = True
+        st.session_state.saved_model = fbmodel
+
+        ####################### SEE RESULTS ########################
+        st.markdown("#### See the results ☑️")
+        st.info("The model is able to forecast energy consumption as well as learn the predicted data's **trend**, **weekly** and **yearly seasonality**.")
+
+        tab1_result, tab2_result, tab3_result, tab4_result = st.tabs(["Performance", "Trend", "Weekly seasonality", "Yearly seasonality"])
+        with tab1_result:
+            # Compute model root mean squared error
+            y_true = test_plot["y"]
+            y_pred = forecast["yhat"]
+            error = str(np.round(root_mean_squared_error(y_true, y_pred, ),3))
+
+            col1, col2 = st.columns([0.1,0.9])
+
+            with col1:
+                st.markdown("")
+                st.metric(label="**Average error**", value=error)
+            
+            with col2:
+                # Create df for true vs predicted plot
+                df_results = pd.concat([test_plot.reset_index(drop=True), forecast.drop(columns=["ds"]).reset_index(drop=True)], axis=1)[["ds","y","yhat"]]
+                df_results = df_results.melt(id_vars="ds")
+                df_results["variable"] = df_results["variable"].map({"y":"true values", "yhat":"predicted values"})
+                df_results.columns = ["Date", "Variable", "Global Active Power"]
+                
+                fig = px.line(df_results, x="Date", y="Global Active Power", color="Variable", 
+                        color_discrete_sequence=["lightblue", "black"], line_dash = 'Variable')
+
+                fig.update_layout(
+                    title=f'True vs predicted power consumption',
+                    width=1200,  
+                    height=600  
+                )
+
+                st.plotly_chart(fig, use_container_width=True)
+
+        with tab2_result:
+            ymin = forecast["trend"].min() 
+            ymax = forecast["trend"].max()
+            
+            fig = px.area(forecast, x="ds", y="trend", color_discrete_sequence=["red"]) #range_y=[ymin, ymax])
+            fig.update_layout(title="Trend", xaxis_title="Date", yaxis_title="Trend")
+            st.plotly_chart(fig, use_container_width=True)
+            st.markdown("""**Interpretation** <br>
+                        No trend in the household's energy consumption has been detected by the model.""", unsafe_allow_html=True)
+
+        with tab3_result:
+            #st.success("**Weekly seasonality** refers to a repeating pattern or variation that occurs on a weekly basis on the energy consumption data.")
+            days_week = dict(zip(np.arange(1,8),["Monday", "Tuedsay", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]))
+            forecast_weekly = forecast.copy()
+            forecast_weekly["dayweek"] = forecast_weekly["ds"].apply(lambda x: x.isoweekday()).map(days_week)
+
+            fig = px.area(forecast_weekly, x="dayweek", y="weekly", color_discrete_sequence=["purple"])
+            fig.update_layout(title="Weekly seasonality", xaxis_title="Date", yaxis_title="Weekly")
+            st.plotly_chart(fig, use_container_width=True)
+
+            st.markdown("""**Interpretation** <br>
+            The household consumes more electrical power during the week-end (Saturday and Sunday) then during the week.           
+                        """, unsafe_allow_html=True)
+
+        with tab4_result:
+            forecast_year = forecast[["ds","yearly"]].copy()
+            forecast_year["ds_year"] = forecast_year["ds"].apply(lambda x: x.strftime("%B %d"))
+            forecast_year["ds"] = forecast_year["ds"].apply(lambda x: x.strftime("%m-%d"))
+            forecast_year.sort_values(by=["ds"], inplace=True)
+            forecast_year = forecast_year.groupby(["ds","ds_year"]).mean().reset_index()
+
+            st.markdown("")
+            ts_chart = alt.Chart(forecast_year, title="Yearly seasonality").mark_area(opacity=0.5,line = {'color':'darkblue'}).encode(
+                x=alt.X('ds_year:T', axis=alt.Axis(format='%b', tickCount=12), title="Date"),
+                y=alt.Y('yearly:Q', title="Yearly seasonality"),
+                ).interactive()
+
+            st.altair_chart(ts_chart, use_container_width=True)
+
+            st.markdown("""**Interpretation** <br>
+            The household consumes more energy during the winter (November to February) and less during the warmer months.           
+                        """, unsafe_allow_html=True)
+            
+
+
+################################## MAKE FUTURE PREDICTIONS ###############################
+
+# st.markdown("#### Forecast new values ")
+
+# st.info("**The model needs to be trained before it can predict new values.**")
+
+# st.
+
+# make_predictions = st.button("**Forecast new values**")
+
+# if make_predictions is True:
+#     if st.session_state.saved_model is True:
+
+
diff --git a/pretrained_models/recommendation_system/model.pkl b/pretrained_models/recommendation_system/model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..ef4f3dee5479d92235197af6e155375d2250db86
--- /dev/null
+++ b/pretrained_models/recommendation_system/model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbfc978fc09b8099d033018cff43b16efce86cc1617f020b96e1b14bc08ebfb9
+size 1633232
diff --git a/pretrained_models/supervised_learning/credit_score_model.pkl b/pretrained_models/supervised_learning/credit_score_model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5ed27bca59006dd93766b726e3eb7b11eb14428b
--- /dev/null
+++ b/pretrained_models/supervised_learning/credit_score_model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82434e04a96b72b75803149105eccc31682c2eba9f7c391678862d94bae40ea3
+size 81909304
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92bfbf2340622f3fdf648aa9a8e244d5318f904f
Binary files /dev/null and b/requirements.txt differ
diff --git a/requirements_ex.txt b/requirements_ex.txt
new file mode 100644
index 0000000000000000000000000000000000000000..306a9e4b0181273c6c9434552b457a8eb2a8bb67
Binary files /dev/null and b/requirements_ex.txt differ
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..102bbba19dc85fd28904b99e30de2214cd31fb44
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,148 @@
+import os 
+import io
+import pickle
+import base64
+import streamlit as st
+import pandas as pd
+
+#from google.oauth2 import service_account
+#from googleapiclient.discovery import build
+from htbuilder import HtmlElement, div, hr, a, p, img, styles
+from pathlib import Path
+
+
+
+######################## LOAD DATA FROM REPO ##########################
+
+@st.cache_data(ttl=3600, show_spinner=False)
+def load_data_pickle(path, file):
+    """Load data from pickle file"""
+    df = pd.read_pickle(os.path.join(path,file))
+    return df
+
+@st.cache_data(ttl=3600, show_spinner=False)
+def load_data_csv(path, file):
+    "Load data from csv file"
+    df = pd.read_csv(os.path.join(path,file))
+    return df
+
+@st.cache_data(ttl=3600, show_spinner=False)
+def load_model_pickle(path, file):
+    """Load model from pickle file"""
+    path_file = os.path.join(path, file)
+    model = pickle.load(open(path_file, 'rb'))
+    return model
+
+
+#################### LOAD DATA FROM GOOGLE DRIVE ###################
+
+# @st.cache_data(ttl=3600, show_spinner=False)
+# def load_data(file, sheet_name, **kwargs):
+#     df = pd.read_excel(file, sheet_name=sheet_name, **kwargs)
+#     return df
+
+# @st.cache_data(ttl=3600, show_spinner=False)
+# def load_model(file):
+#     """Load model from pickle file"""
+#     model = pickle.load(file)
+#     return model
+
+
+# @st.cache_data(show_spinner=False) #3600 seconds
+# def authenticate_drive():
+#     creds = service_account.Credentials.from_service_account_info(
+#         st.secrets["connections_gcs"],
+#         scopes=["https://www.googleapis.com/auth/drive.readonly"]
+#     )
+#     drive_service = build('drive', 'v3', credentials=creds)
+    
+#     return drive_service
+
+# @st.cache_data(ttl=3600, show_spinner=False)
+# def load_content_drive(file_id, _drive_service):
+#     """ Load content from google drive
+#     """
+#     request = _drive_service.files().get_media(fileId=file_id)
+#     file_content = io.BytesIO(request.execute())
+    
+#     return file_content
+    
+
+# @st.cache_data(ttl=3600, show_spinner=False)
+# def load_data_drive(file_content, sheet_name=None, **kwargs):
+#     """ Load data using file_content    
+#     """
+#     if sheet_name is None:
+#         df = pd.read_excel(file_content, **kwargs)
+#     else:
+#         df = pd.read_excel(file_content, sheet_name=sheet_name, **kwargs)
+    
+#     return df
+
+
+# @st.cache_data(ttl=3600, show_spinner=False)
+# def load_model_drive(file_content):
+#     """ Load model using file_content
+#     """
+#     model = pickle.load(file_content)
+#     return model
+
+
+# def files_in_drive(folder_id, drive_service):
+#     results = drive_service.files().list(q=f"'{folder_id}' in parents").execute()
+#     files_dict= results.get('files', [])
+    
+#     return files_dict
+
+
+
+#################### PASSEWORD #####################
+
+def check_password():
+    """Returns `True` if the user had the correct password."""
+
+    def password_entered():
+        """Checks whether a password entered by the user is correct."""
+        if "password" in st.session_state and st.session_state["password"] == st.secrets["password"]:
+            st.session_state["password_correct"] = True
+            del st.session_state["password"]  # don't store password
+        else:
+            st.session_state["password_correct"] = False
+
+    if "password_correct" not in st.session_state:
+        # First run, show input for password.
+        st.text_input(
+            "Password", type="password", on_change=password_entered, key="password"
+        )
+        return False
+    elif not st.session_state["password_correct"]:
+        # Password not correct, show input + error.
+        st.text_input(
+            "Password", type="password", on_change=password_entered, key="password"
+        )
+        st.error("😕 Password incorrect")
+        return False
+    else:
+        # Password correct.
+        return True
+
+
+
+
+
+
+###################### OTHER ######################
+
+def img_to_bytes(img_path):
+    img_bytes = Path(img_path).read_bytes()
+    encoded = base64.b64encode(img_bytes).decode()
+    return encoded
+
+
+def link(link, text, **style):
+    return a(_href=link, _target="_blank", style=styles(**style))(text)
+
+@st.cache_data
+def convert_df(df):
+# IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv().encode('utf-8')
\ No newline at end of file