Movie Recommendation System using Machine Learning

This is a project that aims to develop a movie recommendation system using machine learning techniques. The system will recommend movies to users based on Tags of their past viewing history and preferences.

Table of Contents

Introduction

Technologies Used

Dataset

Demo

Contributing

License

Introduction

The aim of this project is to develop a movie recommendation system that can provide movie recommendations to users based on their past viewing history and preferences. The system uses collaborative filtering and content-based filtering techniques to generate recommendations for users.

Collaborative filtering is a technique that uses the past behavior of users to generate recommendations. It identifies users who have similar preferences and recommends movies that they have liked in the past. Content-based filtering, on the other hand, recommends movies based on their attributes such as genre, cast, and director.

Technologies Used

Python 3.8

Jupyter Notebook

Scikit-learn

Pandas

NumPy

Matplotlib

Seaborn

Dataset

The dataset used in this project is the MovieLens dataset. It contains over 5000 movie ratings from users of the MovieLens website. The dataset can be downloaded from here.

Demo

movie.recommendation.system.mp4

Contributing

Contributions are always welcome! If you have any suggestions or find any issues with the project, please feel free to open an issue or submit a pull request.

License

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