Adaptive-Learning-System.py

# adaptive_learning_system.py

"""
Adaptive Learning System Module for Personalized Learning Path Recommendation System

This module contains functions for implementing an adaptive learning system that adjusts
recommendations based on student progress and learning style.

Techniques Used:
- Reinforcement Learning
- Dynamic Adjustment

Algorithms Used:
- Deep Q-Learning (DQN)
- Contextual Bandits

Libraries/Tools:
- TensorFlow
- Keras
"""

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.preprocessing import StandardScaler
import joblib

class AdaptiveLearningSystem:
    def __init__(self, state_size, action_size):
        """
        Initialize the AdaptiveLearningSystem class.
        
        :param state_size: int, size of the state space
        :param action_size: int, size of the action space
        """
        self.state_size = state_size
        self.action_size = action_size
        self.model = self._build_model()
        self.scaler = StandardScaler()

    def _build_model(self):
        """
        Build the DQN model.
        
        :return: Sequential, the DQN model
        """
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=0.001))
        return model

    def preprocess_data(self, data):
        """
        Preprocess the data for training and prediction.
        
        :param data: DataFrame, input data
        :return: DataFrame, preprocessed data
        """
        data = data.fillna(data.mean())
        data = self.scaler.fit_transform(data)
        return data

    def train(self, data, target_column, epochs=10):
        """
        Train the adaptive learning system using DQN.
        
        :param data: DataFrame, input data
        :param target_column: str, name of the target column
        :param epochs: int, number of epochs for training
        """
        X = data.drop(columns=[target_column])
        y = data[target_column]

        for epoch in range(epochs):
            for i in range(len(X)):
                state = X.iloc[i].values.reshape(1, -1)
                action = y.iloc[i]
                target = self.model.predict(state)
                target[0][action] = action
                self.model.fit(state, target, epochs=1, verbose=0)

        joblib.dump(self.scaler, 'models/scaler.pkl')
        self.model.save('models/adaptive_learning_model.h5')

    def predict(self, student_data):
        """
        Predict the next best action for the student.
        
        :param student_data: DataFrame, data of the student
        :return: int, predicted action
        """
        scaler = joblib.load('models/scaler.pkl')
        model = tf.keras.models.load_model('models/adaptive_learning_model.h5')
        student_data = scaler.transform(student_data)
        action_values = model.predict(student_data)
        return np.argmax(action_values[0])

if __name__ == "__main__":
    data_filepath = 'data/processed/preprocessed_student_data.csv'
    student_data_filepath = 'data/processed/new_student_data.csv'
    target_column = 'learning_path'

    adaptive_system = AdaptiveLearningSystem(state_size=5, action_size=3)  # Example sizes
    data = pd.read_csv(data_filepath)
    data = adaptive_system.preprocess_data(data)

    # Train the adaptive learning system
    adaptive_system.train(data, target_column)

    # Predict the next best action for a new student
    new_student_data = pd.read_csv(student_data_filepath)
    new_student_data = adaptive_system.preprocess_data(new_student_data)
    next_action = adaptive_system.predict(new_student_data)
    print("Next best action for the student:", next_action)