- Gather a dataset of movies, including attributes like title, genre, ratings, and user reviews.
- Analyze the dataset to identify trends, popular genres, and correlations between ratings and genres.
- Use collaborative filtering or content-based filtering to suggest movies based on user preferences.
- Test the model’s accuracy using metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).
Hints:
- Dataset Sources: Check out datasets on platforms like Kaggle or MovieLens.
- Tools: Use Python libraries like Pandas for data manipulation, Matplotlib/Seaborn for visualization, and Scikit-learn for building models.
- Collaborative Filtering: Consider using the Surprise library for building recommendation systems easily.
- Content-Based Filtering: Leverage TF-IDF or word embeddings to analyze movie descriptions for content similarities.
- Deployment: Think about using Flask or Streamlit to create a simple web app to showcase your recommendation system.