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XGBmodel.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5616e5db162a0206f07f7057de10327a04780b3cdaeb548fd003791459853f57
+size 309844

app.py

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+import streamlit as st
+
+# Import functions from eda.py and predict.py
+from prediction import make_prediction
+
+def main():
+    st.sidebar.title("Navigation")
+    selection = st.sidebar.radio("Select", ["Home", "EDA", "Prediction"])
+
+    if selection == "Home":
+        st.title("Welcome to our Laptop Price Predictor!")
+        st.subheader("",divider = 'gray')
+        st.write(
+            "This application is build so you don't have to do too much time on researching laptop prices. "
+            "Just tell me the specs you want and we'll tell you the price estimation!"
+        )
+
+        st.subheader("Prediction Model")
+        st.write(
+            "The Prediction section allows you to input your dream laptop specs. "
+            "By entering details about the laptop, we will give you a price estimation!"
+        )
+        st.markdown(
+            """
+            <style>
+            .highlight {
+                color: #FF5733;
+                font-weight: bold;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+        st.markdown('<p class="highlight">Make informed decisions to enhance your hotel management!</p>', unsafe_allow_html=True)
+
+    elif selection == "Prediction":
+        make_prediction()  # Call prediction function
+
+if __name__ == "__main__":
+    main()

prediction.py

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+import streamlit as st
+import pandas as pd
+import pickle  # or import pickle if that's what you used to save your model
+
+# Load your trained model
+with open('XGBmodel.pkl', 'rb') as file_1:
+    XGBmodel = pickle.load(file_1)
+
+# Set the title of the app
+def  make_prediction():
+    st.title('Laptop Price Prediction')
+    st.write("MLModel Built by MSABP")
+    st.header('',divider='gray')
+
+    # Create a form for inputting laptop specifications
+    with st.form(key='laptop_spec_form'):
+        # Input fields
+        company = st.text_input("Company")
+        product = st.text_input("Product")
+        laptop_type = st.selectbox("Type", ["Notebook", "Gaming", "Ultrabook", "2-in-1", "Others"])
+        inches = st.number_input("Inches", min_value=0, step=1)
+        ram = st.number_input("RAM (GB)", min_value=0, step=2)
+        os = st.text_input("Operating System")
+        weight = st.number_input("Weight (kg)", min_value=0.0, step=0.1)
+        screen = st.selectbox("Screen Resolution", ["Full HD", "4K", "HD", "Others"])
+        width = st.number_input("Screen Width (px)", min_value=0, step=120)
+        height = st.number_input("Screen Height (px)", min_value=0, step=120)
+        touchscreen = st.selectbox("Touchscreen", ["Yes", "No"])
+        ips = st.selectbox("IPS Display", ["Yes", "No"])
+        retina = st.selectbox("Retina Display", ["Yes", "No"])
+        cpu_company = st.text_input("CPU Company")
+        cpu_freq = st.number_input("CPU Frequency (GHz)", min_value=0.0, step=0.1)
+        cpu_model = st.text_input("CPU Model")
+        primary_storage = st.number_input("Primary Storage (GB)", min_value=0, step=64)
+        secondary_storage = st.number_input("Secondary Storage (GB)", min_value=0, step=64)
+        primary_storage_type = st.selectbox("Primary Storage Type", ["SSD", "HDD", "Others"])
+        secondary_storage_type = st.selectbox("Secondary Storage Type", ["No", "SSD", "HDD", "Others"])
+        gpu_company = st.text_input("GPU Company")
+        gpu_model = st.text_input("GPU Model")
+
+        # Submit button
+        submit_button = st.form_submit_button(label='Submit')
+
+        if submit_button:
+            # Create a dictionary from the input data
+            laptop_data = {
+                "Company": company,
+                "Product": product,
+                "Type": laptop_type,
+                "Inches": inches,
+                "Ram": ram,
+                "OS": os,
+                "Weight": weight,
+                "Screen": screen,
+                "Width": width,
+                "Height": height,
+                "Touchscreen": touchscreen,
+                "IPS": ips,
+                "Retina": retina,
+                "CPU_company": cpu_company,
+                "CPU_freq": cpu_freq,
+                "CPU_model": cpu_model,
+                "PrimaryStorage": primary_storage,
+                "SecondaryStorage": secondary_storage,
+                "PrimaryStorageType": primary_storage_type,
+                "SecondaryStorageType": secondary_storage_type,
+                "GPU_company": gpu_company,
+                "GPU_model": gpu_model
+            }
+
+            # Convert the input data to a DataFrame for prediction
+            input_df = pd.DataFrame([laptop_data])
+
+            st.subheader('User Input')
+            st.write("""This is a view of the data you have entered.
+            """)
+            st.write(input_df)
+
+            # Make the prediction
+            predicted_price = XGBmodel.predict(input_df)
+
+            # Display the prediction message
+            st.write(f'Based on specs you wanted, the price estimation is: {predicted_price[0]:,.2f}')
+
+
+
+if __name__ == "__main__":
+    make_prediction()

requirements.txt

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+streamlit==1.32.0
+scikit-learn==1.5.2
+pandas==2.2.3
+seaborn==0.13.2
+matplotlib==3.9.2