A machine learning model predicted student dropout rates and academic success by identifying at-risk students and providing targeted interventions.
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Updated
May 4, 2023 - Jupyter Notebook
A machine learning model predicted student dropout rates and academic success by identifying at-risk students and providing targeted interventions.
The classification problem of student dropout data of an institute
A machine learning project to predict student dropout risks based on demographic, academic, and socio-economic factors. Includes data preprocessing, feature engineering, model training, and deployment scripts. Designed to help educational institutions identify at-risk students and improve retention rates.
Work on IEEE Journal Special Issue on Early Prediction and Supporting of Learning Performance
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