🎬 Movie Recommender System

This is a content-based movie recommender system that suggests similar movies based on a selected title.
It uses movie metadata from TMDb and applies NLP techniques and cosine similarity for recommendations.

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🚀 Features

• ✅ Content-based filtering using movie descriptions
• ✅ Cosine similarity for matching movie vectors
• ✅ Flask-based web application interface
• ✅ Responsive design (works on desktop/mobile)
• ✅ Pre-trained model (no training needed during runtime)

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📁 Project Structure

Movie_Recommender/ │ ├── app.py # Flask app ├── movie_recommander_system.ipynb # Notebook used for model development ├── requirements.txt # Python dependencies ├── setup.sh / procfile # For deployment (Heroku, etc.) ├── tmdb_5000_movies.csv # Dataset ├── tmdb_5000_credits.csv # Dataset └── static/ & templates/ # Flask frontend files (if present) └── .gitignore # ignore unnecessary files

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📦 Installation

1. Clone this repository

git clone https://github.com/mandyhirphode/movie_recommender_system.git
cd movie_recommender_system

2. Install dependencies

pip install -r requirements.txt

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▶️ Run the App

python app.py

Then open http://127.0.0.1:5000/ in your browser.

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🌐 Demo

You can deploy this on Heroku, Render, or any cloud platform.

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📊 Dataset Source

TMDb 5000 Movies and Credits Dataset from Kaggle