🎬 Movie Recommender

A full-stack content-based movie recommendation system that suggests similar movies using NLP techniques like TF-IDF and CountVectorizer. Users can search by title or filter by genre to receive intelligent recommendations based on metadata similarity.

Live Site:
🔗 https://movie-recommender-frontend-i255.onrender.com/

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Technologies Used:

• Backend: Python, Flask, Scikit-learn, Joblib
• Frontend: ReactJS, Vite, Axios
• Deployment: Render (both frontend & backend)

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Features:

• Smart search: Get similar movie recommendations based on title or genre.
• NLP-based logic: Supports both TF-IDF and CountVectorizer techniques for vector similarity.
• Fast API: Optimized model loading for sub-second response time.
• Deployed frontend with real-time search, filter by genre, and responsive design.
• Persistent model storage with Joblib to avoid recomputation and reduce memory usage.

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Screenshots:

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Dev:

Prakash Gupta