Damiieibikun/Student-s-Dropout-Prediction-using-Supervised-Machine-Learning-Classifiers
A simple Data science project on Predicting Student's dropout using Machine Learning classification models
Implements ensemble classification techniques across Decision Tree, Random Forest, and Logistic Regression models trained on 11-year institutional datasets spanning demographic, academic, and socio-economic features, with scikit-learn and pandas pipelines for preprocessing. Identifies enrollment age and course selection as primary dropout predictors, achieving 83% accuracy with Decision Tree classification. Results delivered as reproducible Jupyter notebooks with matplotlib/Plotly visualizations for exploratory analysis and model performance evaluation.
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Jupyter Notebook
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Last pushed
Nov 14, 2023
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