--- a
+++ b/heart.py
@@ -0,0 +1,47 @@
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+# Load dataset (replace with actual dataset path)
+file_path = "PPG_Dataset.csv"  
+df = pd.read_csv(file_path)
+
+# Select only Heart Rate (HR) & SpO₂ as features
+X = df[['Heart_Rate', 'SpO2']].values  
+y = df['Label'].values  # Label: 0 = Normal, 1 = Abnormal
+
+# Normalize input features
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+
+# Reshape for LSTM input (samples, timesteps, features)
+X = X.reshape(X.shape[0], 1, 2)  # 1 timestep, 2 features (HR & SpO₂)
+
+# Split data into training (80%) and testing (20%)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define LSTM model
+model = Sequential([
+    LSTM(64, return_sequences=True, input_shape=(1, 2)),  
+    Dropout(0.2),
+    LSTM(32),
+    Dropout(0.2),
+    Dense(16, activation='relu'),
+    Dense(1, activation='sigmoid')  # Binary classification output
+])
+
+# Compile model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))
+
+# Save the trained model
+model.save("heart_monitor_lstm.h5")
+np.save("scaler.npy", scaler.mean_)  # Save scaler for real-time data
+
+print("✅ Model training complete!")