The goal of this project was to predict student engagement based on their activity during the first four weeks after enrollment.

 

ToU provided a dataset with anonymized behavioral data, including how often students logged in, accessed lessons, submitted assignments, and interacted in discussions.

 

The task was to clean and prepare the data, explore patterns, create meaningful features, and apply machine learning models to classify students as either highly engaged or not engaged.