It involves building and evaluating classification models (Logistic Regression, Decision Tree, Random Forest, XGBoost), covering data cleaning, exploratory analysis, model training, tuning, evaluation ...

This project benchmarks XGBoost and PyTorch models for binary classification across serial CPU, parallel CPU, and GPU setups. Parallel XGBoost achieved the fastest training time without loss in ...