🎬 Movie Recommendation System

This is a content-based movie recommendation system built with Python and FastAPI, using the Bag of Words (BoW) technique.
It recommends movies similar to a given movie by analyzing their overviews (descriptions) and computing similarity scores.

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✨ Features

• Preprocesses movie descriptions with stemming and stopword removal.
• Uses CountVectorizer (BoW) to create feature vectors.
• Computes cosine similarity between movies.
• FastAPI backend with:
  • /ping health-check endpoint
  • /recommend endpoint to get top 5 similar movies
• Modular structure for easy extension.

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🛠️ Tech Stack

• Python
• FastAPI (backend framework)
• scikit-learn (CountVectorizer, cosine similarity)
• NLTK (PorterStemmer)
• Pandas (data handling)

📡 API Endpoints

GET /ping

Health-check endpoint.

Response:

{
  "message": "pong"
}

GET /recommend?movie_id=<id>

Request

{
  "movie_id": 123
}

Response

{
  "recommendations": [
    { "movie_id": 101, "movie_title": "Guardians of the Galaxy" },
    { "movie_id": 202, "movie_title": "John Carter" },
    { "movie_id": 303, "movie_title": "Star Trek" },
    { "movie_id": 404, "movie_title": "The Avengers" },
    { "movie_id": 505, "movie_title": "The Last Airbender" }
  ]
}