🎬 Movie Recommendation System

A smart recommendation engine that suggests similar movies based on a selected title using collaborative filtering (SVD). Built with Streamlit, it fetches posters, displays release year, and visualizes ratings using stars — all powered by the TMDB API.

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🧠 Overview

This app enables users to select a movie and receive top 10 recommendations based on a similarity matrix trained on Netflix movie ratings. It uses:

• SVD (Singular Value Decomposition) for latent factor modeling
• Cosine similarity for identifying similar movies
• TMDB API for fetching posters and metadata
• Streamlit for an interactive UI

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📸 Sample Output

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📁 Folder Structure

📦 Movie-Recommender/
├── app.py                   # Streamlit app
├── svd_model.pkl            # Trained model with similarity matrix
├── movie_titles.csv         # Metadata with Movie_Id, Title, Year
├── netflix_ratings.csv      # Ratings (0–1 format)
├── requirements.txt         # Python dependencies
├── .streamlit/
│   └── secrets.toml         # API key storage (TMDB)
└── screenshots/
    └── sample_ui.png        # UI Preview

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🔧 Setup Instructions

1. Clone the repository

git clone https://github.com/PradeepJami18/movie-recommender.git
cd movie-recommender

2. Create virtual environment (optional but recommended)

python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # macOS/Linux

3. Install dependencies

pip install -r requirements.txt

4. Add your TMDB API Key

Create the following file:

.movie-recommender/
└── .streamlit/
    └── secrets.toml

Inside secrets.toml, paste your TMDB API key like this:

TMDB_API_KEY = "your_tmdb_api_key_here"

📌 Get your TMDB API Key

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🚀 Running the App Locally

streamlit run app.py

Then open http://localhost:8501 in your browser.

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💫 Features

• 🎞️ Poster support via TMDB API
• 🌟 Ratings visualized as 5-star bars (0–1 scaled to 1–5)
• 📅 Movie release year display
• ⚡ Fast response with precomputed similarity matrix
• 🧊 Clean & responsive Streamlit UI

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📦 Requirements

streamlit
pandas
numpy
scikit-learn
tmdbsimple

Install using:

pip install -r requirements.txt

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🌐 Deployment (Optional)

You can deploy this app on Streamlit Community Cloud.

1. Push your code to a GitHub repo.
2. Go to Streamlit Cloud and connect your repo.
3. Add your TMDB API key in App Settings > Secrets like this:

TMDB_API_KEY = "your_tmdb_api_key"

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🙋‍♂️ Author

Jami Pradeep
📧 pradeepjami18@gmail.com
🔗 LinkedIn
📁 GitHub

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📄 License

This project is licensed under the MIT License.
See the LICENSE file for more information.

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📌 Future Improvements

• Add genre-based filtering
• Incorporate user authentication
• Add rating and review submission from users
• Real-time retraining with feedback

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💡 If you like this project, feel free to ⭐ it on GitHub!