Academic Success Prediction Model
Academic-success prediction (Graduate/Dropout/Enrolled) from 24 student features, comparing 6 ML models (RF/XGBoost best, ~63%) — CSE422 AI project.
Overview
A machine-learning study that predicts student academic outcomes — Graduate, Dropout, or Enrolled — from 24 socio-economic, demographic and academic features, so institutions can spot at-risk students early and target support. Built for CSE422 (Artificial Intelligence) on a 4,424-student dataset.
Approach
After cleaning, encoding and scaling the features (with EDA on correlations, outliers and class balance), six classifiers are trained and compared head-to-head — a Keras neural network, Random Forest, XGBoost, K-Nearest Neighbours, Decision Tree and Logistic Regression — each evaluated with accuracy, precision/recall/F1, confusion matrices and ROC/AUC.
Results
The ensemble methods came out on top — Random Forest and XGBoost at ~63% accuracy, with the neural network close behind (~62%) — on a genuinely hard three-class problem with class imbalance. The head-to-head comparison highlights where feature engineering and ensembling help most, and where minority-class prediction still struggles.
By the numbers
Tech stack & key skills
Core tools, methods and skills demonstrated in this project: