hamzaezzine/Predict-students-dropout-and-academic-success-using-machine-learning-algorithms
Predict Students Dropout and Academic Success Using Machine Learning Algorithms
Implements multi-stage data preprocessing using correlation analysis and IQR-based outlier detection on a 4,424-student dataset spanning demographic, socio-economic, macroeconomic, and academic attributes across 17 fields. Trains and compares multiple classification algorithms to predict three-class outcomes (dropout, enrolled, graduate) using feature selection based on target correlation. Provides comprehensive evaluation metrics and visualizations to identify key predictors of student attrition across socio-economic and academic performance dimensions.
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