Student Dropout Prediction – Machine Learning Project Overview

This project predicts student outcomes (Graduate vs Dropout) using a machine-learning pipeline built with Python and Scikit-Learn. The dataset includes demographic, academic, and socioeconomic variables. The model trains two Random Forest classifiers and evaluates them using ROC-AUC.

->Tech Stack

Python Pandas, NumPy Scikit-Learn (RandomForestClassifier, LabelEncoder, StandardScaler) Jupyter / VS Code

->Features Data cleaning & handling missing values (median and most-frequent imputation) Label encoding for categorical features Separate prediction models for multiple target variables Evaluation using ROC-AUC CSV output generation containing prediction probabilities