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movie_recommendation/ │ ├── app.py # Flask API for recommendations ├── movie_ratings.csv # User ratings dataset (generated from u.data) ├── movies.csv # Movies metadata dataset (generated from u.item) ├── requirements.txt # Dependencies

run pip install -r requirements.txt to get the tools.

Running the Application

Ensure MongoDB and Redis are running on your local machine. Run the preprocessing script to convert and load the MovieLens dataset into MongoDB. Run the Flask API: python app.py

Access the API endpoint: http://localhost:5000/recommendations?movie_id=1

This will return the top 5 movie recommendations based on movie ID 1.

About

This project demonstrates a full-stack recommendation engine using collaborative filtering, NoSQL storage (MongoDB), and caching (Redis).

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