🎬 Movie Recommendation System using Streamlit

A modern and fully optimized Streamlit web application that recommends movies based on your input using cosine similarity on movie features. It includes fuzzy matching for typos, real-time suggestions, and a clean landscape UI layout.

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📂 Dataset Used

• Source: TMDB 5000 Movies Dataset

• Columns:

  • id, title, overview, genres, keywords, cast, crew (preprocessed to form content-based features)

Dataset Source: TMDB Kaggle Dataset

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💡 Features

✅ Real-time movie search with auto-suggestions
✅ Typo handling using fuzzy matching (fuzzywuzzy)
✅ Top 5 similar movies displayed dynamically
✅ Random default movie and recommendations on app load
✅ Responsive UI with clean horizontal recommendation layout
✅ Error handling for missing/invalid inputs
✅ Modern design with input+enter+suggestion+action support

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🧰 Tech Stack

• Python 3.13.3
• Pandas, NumPy
• Scikit-learn (TfidfVectorizer, cosine_similarity)
• FuzzyWuzzy for typo correction
• Streamlit for frontend

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🗂️ Project Structure

📁 movie-recommender-app/
├── app.py                    # Main Streamlit app
├── data                      # dataset
├── recommend_pipeline.py            # Core recommendation logic
├── requirements.txt          # Python dependencies
└── README.md                 # Project documentation

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🚀 How to Run the App

1️⃣ Clone the Repository

git clone https://github.com/atharmshaikh/movie-recommender-streamlit-app.git
cd movie-recommender-streamlit-app

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run Streamlit App

streamlit run app.py

The app will open in your browser at http://localhost:8501/.

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🧪 Example Usage

• Type part of a movie name (e.g., misi → auto-suggests Mission to Mars)
• Select or hit Enter → instantly shows top 5 recommended movies
• Handles misspelled inputs with suggestions: Mistion to Mars → Mission to Mars

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🔐 Requirements

streamlit
pandas
scikit-learn
numpy
fuzzywuzzy
python-Levenshtein

Use pip install -r requirements.txt to install all.

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✨ Screenshots

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👤 Author

Athar Shaikh

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📄 License

This project is open-source and free to use under the MIT License.

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🧠 Future Improvements

• Add user ratings & collaborative filtering
• Enable real movie posters via TMDB API
• Deploy on Streamlit Cloud or HuggingFace Spaces

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⭐ Support

If you like this project, give it a ⭐ on GitHub and feel free to fork or contribute!