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Best_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0ec09e35995ba662ab7c7653bb1ece1fb98cddccf8e1e75d14e506f1d3b63a2
+size 12494689

app.py

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+import streamlit as st
+from sklearn.ensemble import GradientBoostingRegressor
+import joblib
+
+model_path = 'Best_model.joblib'
+loaded_model = joblib.load(model_path)
+
+
+# Preprocess input function
+def preprocess_input(input_data):
+    age = input_data['age']
+    bmi = input_data.get('bmi', None)
+    height = input_data.get('height', None)
+    weight = input_data.get('weight', None)
+    children = input_data['children']
+
+    # Convert height to meters based on the selected unit
+    height_unit = input_data.get('height_unit', 'meters')
+    if height is not None and height_unit != 'meters':
+        if height_unit == 'centimeters':
+            height /= 100
+        elif height_unit == 'feet':
+            height *= 0.3048  # 1 foot = 0.3048 meters
+
+    # Calculate BMI if height and weight are provided and height is not zero
+    if height is not None and height != 0 and weight is not None:
+        bmi = weight / (height ** 2)
+
+    # Convert sex to binary representation
+    sex_0 = 1 if input_data['sex'] == 'female' else 0
+    sex_1 = 1 - sex_0
+
+    # Convert smoker to binary representation
+    smoker_0 = 1 if input_data['smoker'] == 'no' else 0
+    smoker_1 = 1 - smoker_0
+
+    # Map region name to numerical representation
+    region_mapping = {'southeast': 1, 'southwest': 2, 'northwest': 3, 'northeast': 4}
+    region = region_mapping.get(input_data['region'], 0)
+
+    # Create binary representations for each region
+    region_1 = 1 if region == 1 else 0
+    region_2 = 1 if region == 2 else 0
+    region_3 = 1 if region == 3 else 0
+    region_4 = 1 if region == 4 else 0
+
+    # Create the formatted input list with 11 features
+    formatted_input = [age, bmi, children, sex_0, sex_1, region_1, region_2, region_3, region_4, smoker_0, smoker_1]
+
+    return formatted_input
+
+
+# Input page
+def input_page():
+    st.title('Health Insurance Price Prediction')
+    st.write('Please fill in the following details:')
+    age = st.number_input('Age', min_value=0, step=1)
+    sex = st.radio('Sex', ('male', 'female'))
+
+    # Side-by-side input for height unit and height
+    col1, col2 = st.columns(2)
+    with col1:
+        height_unit = st.selectbox('Height Unit', ('meters', 'centimeters', 'feet'))
+    with col2:
+        height = st.number_input('Height', min_value=0.0, step=0.01)
+    weight = st.number_input('Weight (in kg)', min_value=0.0, step=0.1)
+
+    # Calculate BMI immediately after entering height and weight if height is not zero
+    bmi = None
+    if height is not None and height != 0.0 and weight is not None:
+        # Convert height based on selected height unit
+        if height_unit != 'meters':
+            if height_unit == 'centimeters':
+                height /= 100
+            elif height_unit == 'feet':
+                height *= 0.3048  # 1 foot = 0.3048 meters
+
+        # Calculate BMI
+        bmi = weight / (height ** 2)
+        st.write(f'BMI: {bmi:.2f}')
+
+    children = st.number_input('Number of Children', min_value=0, step=1)
+    smoker = st.selectbox('Smoker', ('yes', 'no'))
+    region = st.selectbox('Region', ('southeast', 'southwest', 'northwest', 'northeast'))
+
+    if st.button('Predict'):
+        input_data = {'age': age, 'sex': sex, 'height': height, 'weight': weight, 'children': children,
+                      'smoker': smoker, 'region': region, 'bmi': bmi, 'height_unit': height_unit}
+        processed_input = preprocess_input(input_data)
+        charges = loaded_model.predict([processed_input])[0]
+        st.write('## Estimated Charges')
+        st.write(f'Estimated Charges: ${charges:.2f}', unsafe_allow_html=True)
+        st.write('The following value is estimated based on historical data and predictive modeling techniques and may not represent the exact amount.')
+
+
+# Main function
+def main():
+    input_page()
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+scikit-learn
+pandas
+numpy
+matplotlib
+joblib