Overview: In this post, I walk through the process of building a collaborative filtering movie recommendation system using Python and the IMDb dataset. I cover data preprocessing, model selection, evaluation, and deployment tips.
1. Data Collection & Preprocessing
I started by collecting movie ratings data from IMDb. After cleaning and formatting the data, I explored user and movie statistics to understand the dataset's structure and sparsity.
2. Building the Model
I implemented a collaborative filtering approach using the Surprise library. I experimented with different algorithms (SVD, KNN) and tuned hyperparameters for best results.
3. Evaluation
Model performance was evaluated using RMSE and precision@k. I also visualized recommendations for sample users to ensure the system made relevant suggestions.
4. Deployment
Finally, I wrapped the model in a simple Flask web app for interactive recommendations. I discuss deployment options and best practices for sharing your work.
Want the code? Check out the GitHub repo (coming soon).