Movie Recommendation System

A Flask-based movie recommendation system that provides intelligent movie suggestions using TF-IDF and collaborative filtering. This project includes a production-ready REST API and a simple web interface.

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Features

• Recommend movies based on content similarity (TF-IDF).
• Collaborative filtering for personalized recommendations.
• RESTful API for integration with other applications.
• Simple web interface to test movie recommendations.
• Uses MongoDB for storing and managing movie data.

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Tech Stack

• Backend: Python, Flask
• Database: MongoDB
• Machine Learning: scikit-learn (TF-IDF, cosine similarity)
• Frontend: HTML, CSS, JavaScript
• Deployment: Docker (optional)

Installation

1. Clone the repository:

git clone https://github.com/coderpheonix/movie-recommendation-system.git
cd movie-recommendation-system
Create a virtual environment and activate it:

bash
Copy code
python -m venv venv
source venv/bin/activate       # Linux/macOS
venv\Scripts\activate          # Windows
Install dependencies:

bash
pip install -r requirements.txt
Ensure MongoDB is running locally or update the connection string in config/db_config.py.

Usage
Run the Flask app:

bash
python app.py
Open a web browser and go to:

cpp
http://127.0.0.1:5000
Search for a movie and get recommendations.

API Endpoints
Method	Endpoint	Description
GET	/	Home page
POST	/recommend	Get movie recommendations based on input

Example POST request:

json

{
  "movie_title": "Inception"
}
Example response:

json

{
  "recommended_movies": [
    "Interstellar",
    "The Dark Knight",
    "Memento"
  ]
}
Contributing

Fork the repository.

Create a branch: git checkout -b feature/your-feature-name

Make changes and commit: git commit -m "Add feature"

Push to branch: git push origin feature/your-feature-name

Open a Pull Request.

License
This project is licensed under the MIT License.