A comprehensive machine learning(binary classification) project for detecting credit fraud on a highly imbalanced dataset.

Identifying rare event.

This is an end to end machine learning project using my personal shopping data collected over the past three years.

Stroke prediction using machine learning (LogReg, RF, GBoost, LightGBM) with class imbalance handling and threshold optimisation

This repository focuses on credit card fraud detection using machine learning models, addressing class imbalance with SMOTE & undersampling, and optimizing performance via Grid Search & RandomizedSearchCV. It explores Logistic Regression, Random Forest, Voting Classifier, and XGBoost. balancing precision-recall trade-offs for fraud detection.

Fraud detection pipeline with Logistic Regression, Random Forest, and SMOTE — tuned for business trade-offs, evaluated with PR-AUC, precision, and recall.