Upload train_models.py

train_models.py

@@ -0,0 +1,71 @@
+
+# train_models.py
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from xgboost import XGBClassifier
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM
+import joblib
+import os
+
+# Create models directory if it doesn't exist
+os.makedirs("models", exist_ok=True)
+
+# Load data
+data = pd.read_csv("heart.csv")
+X = data.drop('target', axis=1)
+y = data['target']
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scale features
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Save scaler
+joblib.dump(scaler, "models/scaler.sav")
+
+# 1. Logistic Regression
+print("Training Logistic Regression...")
+lr_model = LogisticRegression(max_iter=1000)
+lr_model.fit(X_train_scaled, y_train)
+joblib.dump(lr_model, "models/logistic_model.sav")
+
+# 2. XGBoost
+print("Training XGBoost...")
+xgb_model = XGBClassifier()
+xgb_model.fit(X_train_scaled, y_train)
+joblib.dump(xgb_model, "models/xgb_model.sav")
+
+# 3. ANN (Artificial Neural Network)
+print("Training ANN...")
+ann_model = Sequential([
+    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
+    Dense(32, activation='relu'),
+    Dense(1, activation='sigmoid')
+])
+ann_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+ann_model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, verbose=0)
+ann_model.save("models/ann_model.h5")
+
+# 4. LSTM (requires reshaping for time-series-like data)
+print("Training LSTM...")
+# Reshape data for LSTM (samples, timesteps, features)
+X_train_lstm = X_train_scaled.reshape(X_train_scaled.shape[0], 1, X_train_scaled.shape[1])
+X_test_lstm = X_test_scaled.reshape(X_test_scaled.shape[0], 1, X_test_scaled.shape[1])
+
+lstm_model = Sequential([
+    LSTM(64, input_shape=(1, X_train_scaled.shape[1])),
+    Dense(1, activation='sigmoid')
+])
+lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+lstm_model.fit(X_train_lstm, y_train, epochs=50, batch_size=32, verbose=0)
+lstm_model.save("models/lstm_model.h5")
+
+print("All models trained and saved successfully!")