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--- |
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title: Recommender system and customer segmentation |
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emoji: 🐨 |
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colorFrom: purple |
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colorTo: blue |
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sdk: streamlit |
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sdk_version: 1.10.0 |
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app_file: recommender_system.py |
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pinned: false |
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license: mit |
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--- |
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# Recommender system and customer segmentation |
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Demo with recsys and clustering for the [online retail](https://www.kaggle.com/datasets/vijayuv/onlineretail?select=OnlineRetail.csv) dataset. |
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## Objective |
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Recommender system: |
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1. interactively select a user |
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2. show all the recommendations for the user |
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3. explain why we get these suggestions (which purchased object influences the most) |
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4. plot the purchases and suggested articles |
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Clustering: |
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1. compute the user clustering |
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2. plot users and their clusters |
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3. explain the meaning of the clusters (compute the mean metrics or literally explain them) |
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## Setup |
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In your terminal run: |
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```bash |
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# Enable the env |
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source .venv/bin/activate |
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# Install the dependencies |
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pip install -r requirements.txt |
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# Or install the freezed dependencies from the requirements_freezed.txt |
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# You are ready to rock! |
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``` |
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## Run |
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In your terminal run: |
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```bash |
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streamlit run recommender_system.py |
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# Now the defualt browser will be opened with |
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# the stramlit page. It you want to customize the |
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# execution of streaming, refer to its documentation. |
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``` |
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## Resources |
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- [streamlit](https://streamlit.io/) |
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- [implicit](https://github.com/benfred/implicit), recsys library |
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- [t-sne guide](https://distill.pub/2016/misread-tsne/) |
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- [RFM segmentation](https://www.omniconvert.com/blog/rfm-score/) |
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