Dropout Prediction with Supervised Learning

Student dropout is a critical issue in the education sector, impacting institutional efficiency and student success. This project, Dropout Prediction with Supervised Learning, leverages machine learning models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), and Naïve Bayes (NB)—to predict student dropouts based on historical academic, demographic, and behavioural data. The study involves data preprocessing, feature selection, and model evaluation to identify key factors influencing dropout rates. Supervised learning techniques are employed to classify students into "at-risk" and "not at-risk" categories. The performance of each model is assessed using accuracy, precision, recall, and F1-score metrics to determine the most effective predictor. The findings aim to provide educational institutions with actionable insights, enabling early intervention strategies such as academic counselling and financial aid support. By implementing predictive analytics, institutions can enhance student retention and improve overall educational outcomes. Keywords – Dropout Prediction, Supervised Learning, Machine Learning Models, Student Retention, Predictive Analytics, Classification Algorithms