| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| import streamlit as st | |
| def preprocess_data(df): | |
| df.replace({'sex': {'male': 0, 'female': 1}}, inplace=True) | |
| df.replace({'smoker': {'yes': 0, 'no': 1}}, inplace=True) | |
| df.replace({'region': {'southeast': 0, 'southwest': 1, 'northwest': 2, 'northeast': 3}}, inplace=True) | |
| def calculate_bmi(weight, height): | |
| if height == 0: | |
| return 0 | |
| height_in_meters = height / 100 | |
| bmi = weight / (height_in_meters ** 2) | |
| return bmi | |
| def main(): | |
| st.title("Medical Insurance Prediction Model") | |
| st.sidebar.header('USER INPUT PARAMETERS') | |
| p1 = st.sidebar.slider("Enter Your Age", 18, 100) | |
| s1 = st.sidebar.selectbox("Sex", ["Male", "Female"]) | |
| p2 = 1 if s1 == "Male" else 0 | |
| weight = st.sidebar.number_input("Enter Your Weight (kg)", min_value=0.0, max_value=200.0) | |
| height_feet = st.sidebar.number_input("Enter Your Height (feet)", min_value=0.0, max_value=8.0) | |
| height_cm = height_feet * 30.48 | |
| calculated_bmi = calculate_bmi(weight, height_cm) | |
| p3_placeholder = st.sidebar.empty() | |
| p3_placeholder.text(f"Calculated BMI: {round(calculated_bmi, 2)}") | |
| p3 = st.sidebar.number_input("this is Your BMI Value", float(calculated_bmi), float(medical_df['bmi'].max())) | |
| p4 = st.sidebar.slider("Enter Number of Children", 0, 5) | |
| s2 = st.sidebar.selectbox("Smoker", ["Yes", "No"]) | |
| p5 = 1 if s2 == "Yes" else 0 | |
| region_mapping = {0: 'Southeast', 1: 'Southwest', 2: 'Northwest', 3: 'Northeast'} | |
| region_options = list(region_mapping.values()) | |
| p6 = st.sidebar.selectbox("Enter Your Region", region_options) | |
| if st.sidebar.button('Predict'): | |
| p6_numeric = list(region_mapping.keys())[list(region_mapping.values()).index(p6)] | |
| input_data = np.array([p1, p2, calculated_bmi, p4, p5, p6_numeric]).reshape(1, -1) | |
| prediction = lg.predict(input_data) | |
| st.balloons() | |
| st.success(f'Medical Insurance prediction : {round(prediction[0], 2)}') | |
| if __name__ == '__main__': | |
| csv_file_path = 'insurance.csv' | |
| medical_df = pd.read_csv(csv_file_path) | |
| preprocess_data(medical_df) | |
| X = medical_df.drop('charges', axis=1) | |
| y = medical_df['charges'] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=2) | |
| lg = LinearRegression() | |
| lg.fit(X_train, y_train) | |
| main() | |