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A Streamlit app that uses a hybrid model combining neural networks and random forests to predict medical insurance costs.

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+import matplotlib.pyplot as plt
+
+# Load and display dataset
+@st.cache_data
+def load_data():
+    data = pd.read_csv("insurance.csv")  # Ensure insurance.csv is in the same directory
+    return data
+
+data = load_data()
+st.title("Medical Insurance Cost Prediction with Hybrid Model")
+st.write("Dataset preview:")
+st.write(data.head())
+
+# Preprocessing and Feature Engineering
+st.subheader("Data Preprocessing and Feature Engineering")
+data['age_smoker'] = data['age'] * data['smoker'].apply(lambda x: 1 if x == 'yes' else 0)
+data['bmi_smoker'] = data['bmi'] * data['smoker'].apply(lambda x: 1 if x == 'yes' else 0)
+
+# Encode categorical variables
+label_encoder = LabelEncoder()
+data['sex'] = label_encoder.fit_transform(data['sex'])
+data['smoker'] = label_encoder.fit_transform(data['smoker'])
+data['region'] = label_encoder.fit_transform(data['region'])
+
+# Select features
+X = data[['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'age_smoker', 'bmi_smoker']]
+y = data['charges']
+
+# Standardize numerical features
+scaler = StandardScaler()
+X[['age', 'bmi', 'children', 'age_smoker', 'bmi_smoker']] = scaler.fit_transform(X[['age', 'bmi', 'children', 'age_smoker', 'bmi_smoker']])
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Define the neural network model
+def create_neural_network():
+    model = Sequential([
+        Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
+        Dropout(0.3),
+        Dense(64, activation='relu'),
+        Dense(1)
+    ])
+    model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
+    return model
+
+st.subheader("Training the Neural Network")
+nn_model = create_neural_network()
+nn_model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2, verbose=1)
+
+# Generate predictions from the neural network for train and test sets
+nn_train_pred = nn_model.predict(X_train).flatten()
+nn_test_pred = nn_model.predict(X_test).flatten()
+
+# Add NN predictions as a new feature for Random Forest
+X_train_rf = X_train.copy()
+X_test_rf = X_test.copy()
+X_train_rf['nn_pred'] = nn_train_pred
+X_test_rf['nn_pred'] = nn_test_pred
+
+# Train a Random Forest on this new feature set
+st.subheader("Training the Random Forest with Neural Network Predictions")
+rf_model = RandomForestRegressor(n_estimators=200, max_depth=12, random_state=42)
+rf_model.fit(X_train_rf, y_train)
+final_predictions = rf_model.predict(X_test_rf)
+
+# Model evaluation
+rmse = np.sqrt(mean_squared_error(y_test, final_predictions))
+r2 = r2_score(y_test, final_predictions) * 100
+st.write(f"RMSE (Root Mean Squared Error): {rmse:.2f}")
+st.write(f"R² (Accuracy): {r2:.2f}%")
+
+# Plot actual vs predicted values
+st.subheader("Actual vs Predicted Values")
+plt.figure(figsize=(10, 5))
+plt.plot(y_test.values, label="Actual Values", color='blue')
+plt.plot(final_predictions, label="Predicted Values", color='orange')
+plt.xlabel("Sample Index")
+plt.ylabel("Insurance Charges")
+plt.legend()
+st.pyplot(plt)
+
+# Prediction on new data
+st.subheader("Predict on New Data")
+input_data = {col: st.number_input(f"Enter {col}:", value=float(X[col].mean())) for col in X.columns}
+
+if st.button("Predict Insurance Charge"):
+    input_df = pd.DataFrame([input_data])
+    input_df[['age', 'bmi', 'children', 'age_smoker', 'bmi_smoker']] = scaler.transform(
+        input_df[['age', 'bmi', 'children', 'age_smoker', 'bmi_smoker']])
+    
+    # Use neural network to predict intermediate feature
+    nn_feature = nn_model.predict(input_df).flatten()
+    input_df['nn_pred'] = nn_feature
+    
+    # Predict final charge using Random Forest
+    final_prediction = rf_model.predict(input_df)
+    st.write(f"Predicted Insurance Charge: ${final_prediction[0]:.2f}")

insurance.csv

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requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+pandas
+numpy
+scikit-learn
+tensorflow
+matplotlib