🎬 CineMatch - Movie Recommendation System

A content-based movie recommendation system that suggests films based on genre, plot similarity, and user ratings. Built with Python and Streamlit.

✨ Features

• Personalized Recommendations: Get 5 similar movies based on your selection
• Smart Filtering: Filter by genre, release year, and minimum rating
• Rich Movie Details: View posters, ratings, and overviews
• Explore Sections:
  • Most viewed movies
  • Highest rated films
  • Recent releases
• Responsive UI: Beautiful Streamlit interface with dark/light mode support

🛠️ Technologies Used

• Python 3 (Pandas, NumPy, scikit-learn)
• Streamlit for web interface
• TMDB API for movie data and posters
• Cosine Similarity for recommendation algorithm
• CountVectorizer for text processing

🚀 Installation

1. Clone the repository:

git clone https://github.com/yourusername/CineMatch.git
cd CineMatch

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file and add your TMDB API key:

TMDB_API_KEY=your_api_key_here

5. Run the app:

streamlit run app.py

📂 Project Structure

CineMatch/
├── app.py                # Main Streamlit application
├── Main.ipynb            # Jupyter notebook for model development
├── movie_data.pkl        # Processed movie data and similarity matrix
├── requirements.txt      # Python dependencies
├── .env.example          # Environment variables template
└── assets/               # Images and screenshots