CineWhiz is an intelligent movie recommendation system that helps users discover films tailored to their preferences using advanced machine learning techniques. Whether you're a fan of thrillers, romantic dramas, or indie films, CineWhiz has something smart to suggest.
- 🎯 Personalized movie recommendations
- 📊 Content-based and collaborative filtering models
- 🧠 Machine Learning/Deep Learning powered suggestions
- 📁 Interactive interface (CLI/Web UI if applicable)
- 🔎 Genre, rating, and user-preference filtering
| Technology | Purpose |
|---|---|
| Python | Core language for backend & ML |
| Pandas, NumPy | Data preprocessing and analysis |
| Scikit-learn | ML algorithms (e.g., KNN, SVD) |
| Flask / Streamlit (if applicable) | Web interface |
| Jupyter Notebook | Prototyping & visualization |
CineWhiz/ │ ├── netflix_data.csv/ # Movie datasets (CSV/JSON) ├── movie-recommendation-system.ipynb/ # Recommendation algorithms └── README.md # Project documentation
- Data Collection: Movie metadata, ratings, genres, user preferences.
- Preprocessing: Cleaning, encoding, normalization.
- Modeling: Using algorithms like:
- Cosine Similarity
- K-Nearest Neighbors
- Singular Value Decomposition
- Recommendation: Top N recommendations are generated based on user similarity or movie content.
| User Input | Recommendations |
|---|---|
| Likes "Inception", "Matrix" | Interstellar, Tenet, The Prestige |
| Likes "Titanic", "Notebook" | Me Before You, A Walk to Remember |
- Python 3.7+
- pip (Python package installer)
TMDb 5000 Movie Dataset
MovieLens Dataset
Add user authentication for personalized history
Integrate real-time ratings
Deploy to cloud (Heroku / Vercel / AWS)
Build mobile-friendly interface
Shreemayee Saha🌻