MLH Movie Recommender

Web application shows personalized movie recommendations

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Inspiration

This application is a full-stack web application that provides personalized movie recommendations. The frontend is built with ReactJS, and the backend is powered by a Python-based Flask API that processes movie data to generate recommendations.

What it does

This project demonstrates a simple yet effective movie recommendation system. Users can input the name of a movie they like, and the system will suggest similar movies based on the input. The recommendation logic leverages the TF-IDF Vectorizer and Cosine Similarity to find movies that closely match the user’s taste.

How we built it

  • Frontend: ReactJS, Axios
  • Backend: Python, Flask, Flask-CORS, Scikit-learn, Pandas, NumPy
  • Data: Movie dataset in CSV format

Challenges we ran into

  • Movie Recommendations: Get a list of movies similar to your favorite ones.
  • Interactive UI: A clean and simple interface built with ReactJS.
  • Real-time API: Fetches recommendations instantly using a Flask backend.

Accomplishments that we're proud of

The Project is completed on time.

What we learned

The recommendation logic leverages the TF-IDF Vectorizer and Cosine Similarity to find movies that closely match the user’s taste. Learnt about Cosine Similarity Topic.

What's next for MLH Movie Recommender

  • Changes in UI
  • Update in Features Selection
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