A machine learning pipeline for predicting steel quality scores using ensemble techniques (LightGBM, XGBoost, CatBoost) and feature engineering.

Supply chain late delivery risk classifier · No leakage · No overfitting · LGB + XGB + CatBoost stacking · SHAP

An end-to-end Machine Learning pipeline utilizing XGBoost to predict match outcomes for both Men's (MNCAA) and Women's (WNCAA) Basketball Tournaments based on historical seeds and regular-season point differentials.

This project was developed at Esprit School of Engineering – Tunisia as part of the PIDEV program (Academic Year 2025–2026). Technologies: React, Spring Boot, AI, YOLOv8.

ML Project