FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

End-to-end customer churn prediction project using the Telco dataset. Includes EDA, data preprocessing, Logistic Regression / Random Forest / XGBoost model comparison, SHAP explainability, and a production-ready prediction pipeline.

This project employs Logistic Regression for binary classification, to predict whether a borrower is capable of repaying a loan based on various financial and demographic factors.

I built this application to allow users to input various clinical parameters and receive an instant prediction of whether a patient is likely to have CKD. The app is hosted on Streamlit community cloud for public access.