Movie Recommendation System

Welcome to the Movie Recommendation System project! This system provides personalized movie recommendations using collaborative filtering, content-based filtering, and traditional machine learning models.

Overview

The Movie Recommendation System is built to help users discover movies they might enjoy based on their preferences and similarities between movies. It employs various recommendation techniques, including collaborative filtering, content-based filtering, and traditional machine learning models trained on movie metadata.

Report and Video

For project report..Click here

For video and report you can also visit: https://abhinav232004.github.io/PRML/

Features

• Multiple Recommendation Techniques: The system utilizes collaborative filtering, content-based filtering, and traditional machine learning models for recommendation.
• Model Evaluation: Performance metrics such as RMSE and MAE are used to evaluate and compare the recommendation models.
• Interactive Interface: The system is built using Streamlit, offering an interactive and intuitive user experience.

Installation

To run the Movie Recommendation System locally, follow these steps:

1. Clone the repository: git clone https://github.com/TejasGupta-27/Movie_Recommendation_System-.git
2. Navigate to the project directory: cd movie-recommendation-system
3. Install dependencies: pip install -r requirements.txt
4. Run the Streamlit app: streamlit run app.py
5. Access the app via the provided URL in your browser.

Usage

• Select the recommendation type: Collaborative Filtering, Content-Based Filtering, or Traditional ML Models.
• Provide the required inputs (e.g., user ID, movie ID).
• Click the button to get recommendations.

Data

The MovieLens dataset is used for this project, containing movie ratings and metadata. The dataset files (movies.csv, ratings.csv) are included in the repository.

Traditional ML Models

In addition to collaborative filtering and content-based filtering, the system incorporates traditional machine learning models trained on movie metadata. These models analyze features such as movie genres and titles to provide recommendations.

Model Comparison

Performance of all recommendation techniques is evaluated using metrics such as RMSE and MAE. The results are compared to identify the most effective approach for generating movie recommendations.

Contributing

Contributions to the Movie Recommendation System project are welcome! If you'd like to contribute, please follow the steps outlined in the CONTRIBUTING.md file.

License

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

Contact

For questions or inquiries about the Movie Recommendation System, feel free to contact