📊 A comprehensive comparison of TabNet and XGBoost across binary classification, multiclass classification, and regression tasks, showcasing performance metrics and fine-tuning results.

Employ the Spain's European Health Survey to predict risk of depression/anxiety

PLAYER RATING ANALYSIS

A machine learning–based credit card fraud detection system using a hybrid ensemble of Random Forest and XGBoost. The project handles highly imbalanced data using SMOTE, performs feature engineering, threshold optimization, and evaluates performance and confusion matrices. Includes model saving and real-time fraud prediction

Data science project predicting SuperStore sales with Linear Regression and XGBoost. Uses date-based, engineered (discount, competitor price), and encoded categorical features. Includes preprocessing, MSE evaluation, and visualizations. Part of #60DaysOfLearning2025.

Classifying whether an asteroid is hazardous or not.

Timeseries analysis - Study case of a small business using ARIMA,SARIMA and XGBoost models to predict stock based on sales.

A loan default payment model

Short analysis of the UCI heart disease analysis, as well as walking through building a predictive model gradient boosted regression model.

📊 Predicting telecom customer churn to enable targeted retention campaigns — XGBoost & feature engineering.