train_rnn.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense, LSTM, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import joblib
import os

# 1. Load Data
def load_data(file_path):
    df = pd.read_csv(file_path)
    print("Dataset Head:")
    print(df.head())
    return df

# 2. Preprocessing
def preprocess_data(df):
    X = df[['number_courses', 'time_study']].values
    y = df['Marks'].values.reshape(-1, 1)

    # Scaling
    scaler_X = MinMaxScaler()
    scaler_y = MinMaxScaler()

    X_scaled = scaler_X.fit_transform(X)
    y_scaled = scaler_y.fit_transform(y)

    # Split
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)

    # Reshape for RNN: (samples, time_steps, features)
    # Here time_steps = 1
    X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
    X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))

    return X_train, X_test, y_train, y_test, scaler_X, scaler_y

# 3. Build Model
def build_model(input_shape):
    model = Sequential([
        LSTM(64, activation='relu', input_shape=input_shape, return_sequences=True),
        Dropout(0.2),
        LSTM(32, activation='relu'),
        Dense(16, activation='relu'),
        Dense(1) # Output for regression
    ])
    
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model

# 4. Main Execution
if __name__ == "__main__":
    file_path = 'Student_Marks.csv'
    if not os.path.exists(file_path):
        print(f"Error: {file_path} not found.")
        exit()

    df = load_data(file_path)
    X_train, X_test, y_train, y_test, scaler_X, scaler_y = preprocess_data(df)

    print(f"X_train shape: {X_train.shape}")
    print(f"y_train shape: {y_train.shape}")

    model = build_model((X_train.shape[1], X_train.shape[2]))
    model.summary()

    # Training
    print("\nStarting training...")
    history = model.fit(
        X_train, y_train,
        epochs=100,
        batch_size=8,
        validation_split=0.1,
        verbose=1
    )

    # Evaluation
    print("\nEvaluating model...")
    loss, mae = model.evaluate(X_test, y_test)
    print(f"Test Loss (MSE): {loss:.4f}")
    print(f"Test MAE: {mae:.4f}")

    # Plot History
    plt.figure(figsize=(10, 5))
    plt.plot(history.history['loss'], label='Train Loss')
    plt.plot(history.history['val_loss'], label='Val Loss')
    plt.title('Model Loss (MSE)')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.savefig('loss_plot.png')
    print("Loss plot saved as 'loss_plot.png'")

    # Predictions
    y_pred_scaled = model.predict(X_test)
    y_pred = scaler_y.inverse_transform(y_pred_scaled)
    y_actual = scaler_y.inverse_transform(y_test)

    # Compare first 5
    print("\nSample Predictions:")
    for i in range(5):
        print(f"Actual: {y_actual[i][0]:.2f}, Predicted: {y_pred[i][0]:.2f}")

    # Save Model and Scalers
    model.save('student_marks_rnn_model.h5')
    joblib.dump(scaler_X, 'scaler_X.pkl')
    joblib.dump(scaler_y, 'scaler_y.pkl')
    print("\nModel saved as 'student_marks_rnn_model.h5'")
    print("Scalers saved as 'scaler_X.pkl' and 'scaler_y.pkl'")