Student Performance Prediction using Machine Learning

A Machine Learning project for analyzing and predicting student academic performance.

The project includes exploratory data analysis, data preprocessing, feature analysis, and training multiple machine learning models for two tasks:

• Regression: Predicting students’ exam scores.

• Classification: Predicting whether a student is likely to pass or fail.

Several machine learning algorithms were trained and compared using Cross-Validation and evaluation metrics such as MAE, RMSE, R², Accuracy, Precision, Recall, and F1-Score.

The project also includes data visualization and model saving for future use.