The objective of this project is to predict student dropout risks using learning analytics and machine learning models. The project aims to identify academic, behavioral, and socio-economic factors influencing dropout rates and support early intervention strategies in educational institutions.
Collect multi-dimensional educational datasets including academic records and engagement metrics.
Perform advanced data preprocessing and normalization.
Conduct exploratory data analysis to identify dropout indicators.
Apply feature selection techniques to reduce dimensionality.
Implement classification models such as logistic regression, decision trees, and random forest.
Address class imbalance using resampling techniques.
Evaluate model performance using precision, recall, F1-score, and ROC curves.
Interpret model outputs to identify high-risk student profiles.
Visualize dropout prediction results using dashboards.
Document ethical considerations, bias handling, and system limitations.
