Hybrid Recommendation System Project Overview This project is a Hybrid Recommendation System for an educational platform. It combines Collaborative Filtering and Content-Based Filtering to recommend lessons to users based on their interaction data and lesson attributes. The system aims to enhance the user experience by providing personalized lesson recommendations.

Key Components: Collaborative Filtering: Recommends lessons by identifying similar users based on their past ratings. Content-Based Filtering: Recommends lessons based on the similarity of lesson attributes (e.g., topics, difficulty). Hybrid Model: Combines both collaborative and content-based methods for improved recommendations.

Installation

1. Clone the Repository To start using the project, first clone the repository to your local machine:

git clone https://github.com/faezehzand/Computer-Science-Project.git cd Computer-Science-Project

2. Set Up a Virtual Environment (Optional) It’s recommended to create a virtual environment to manage dependencies:

Create a virtual environment

python -m venv venv

Activate the virtual environment

source venv/bin/activate # For Linux/Mac venv\Scripts\activate # For Windows

3. Install Dependencies pip install pandas numpy scikit-learn matplotlib

Run Instructions Here’s how to run the different parts of the project.

1. Generate Simulated Dataset Generate a dataset of user interactions and lesson attributes using this script: python generate_simulated_dataset.py
2. Preprocess Data Preprocess the data for analysis and model training: python preprocessing_data.py
3. Exploratory Data Analysis (Optional) To visualize and understand the dataset: python exploratory_data_analysis.py
4. Run the Hybrid Recommendation System Generate lesson recommendations using the hybrid recommendation model: python make_prediction.py

Function Documentation Below is a summary of the main functions in the project:

generate_simulated_dataset.py Description: Generates a simulated dataset with user interactions and lesson attributes. Functions: generate_interactions(): Simulates interactions between users and lessons. generate_lessons(): Creates a dataset with lesson metadata (e.g., topics, difficulty).

preprocessing_data.py Description: Preprocesses the dataset for analysis. Functions: preprocess_interactions(): Cleans and processes user-lesson interactions. preprocess_lessons(): Prepares lesson metadata for the content-based filtering model.

make_prediction.py Description: Implements collaborative filtering, content-based filtering, and hybrid recommendations. Functions: recommend_collaborative(user_id, num_recommendations=5): Generates lesson recommendations using collaborative filtering. recommend_content_based(lesson_id, num_recommendations=5): Recommends lessons based on their similarity to a given lesson. hybrid_recommendations(user_id, num_recommendations=5): Combines both collaborative and content-based methods for hybrid recommendations.

evaluate_models.py Description: Evaluates the performance of the recommendation models. Functions: evaluate_model(y_true, y_pred, model_name): Computes precision, recall, F1-score, and MSE for the models.

Special thanks for your guidance and valuable feedback throughout the project.