In this project, I explored the fascinating intersection of machine learning and film. I built machine learning models and trained them on a collection of movies to assess how likely I am to re-watch some of my favorite films.

Utilizing pairwise comparison and feature engineering, I created preference data and features encompassing themes such as: Genre, Runtime, Budget, and More!

I trained algorithms using this data, including Random Forest and XGBoost to enhance the accuracy of predictions. I then applied the best model to additional movies to generate new predictions and assess their re-watch potential.

Check out the project’s GitHub Repository to see the Python script driving this analysis and the accompanying visuals linked below to learn more about my movie preferences!