Movie Recommendation System

A machine learning-based movie recommendation system that suggests personalized movie recommendations based on user preferences and movie characteristics.

Project Overview #

This project implements a content-based and collaborative filtering recommendation system using the MovieLens dataset. The system analyzes movie features, user ratings, and credits to provide personalized movie recommendations.

Dataset #

The project uses the following datasets:

• movies.csv: Contains movie metadata including titles, genres, and release dates
• ratings.csv: Contains user ratings for movies
• credits.csv: Contains cast and crew information for movies
• movies_small.csv: A smaller subset of the movies dataset for testing

Features #

• Content-based filtering using movie features
• Collaborative filtering using user ratings
• Movie similarity analysis
• Personalized recommendations based on user preferences
• Support for both movie-to-movie and user-to-movie recommendations

Getting Started #

Prerequisites #

• Python 3.x
• Required Python packages (install using pip install -r requirements.txt):
  • pandas
  • numpy
  • scikit-learn
  • jupyter

Installation #

1. Clone the repository:

git clone https://github.com/AhmadTawil1/movie-recomendation-system.git
cd movie-recomendation-system

2. Install required packages:

pip install -r requirements.txt

3. Run the Jupyter notebooks:

jupyter notebook

Usage #

The project consists of three main notebooks:

1. Notebook 1.ipynb: Data preprocessing and exploration
2. Notebook 2.ipynb: Content-based filtering implementation
3. Notebook 3.ipynb: Collaborative filtering implementation

Contributing #

Contributions are welcome! Please feel free to submit a Pull Request.

License #

This project is licensed under the MIT License - see the LICENSE file for details.

Author #

Ahmad Tawil