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

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

Identifying rare event.

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

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.

I developed a model that predicts recipe popularity using nutritional data. The workflow covers cleaning, preprocessing, model training, and tuning the threshold to maximise recall. The final model achieved 99.4 percent recall, supporting the goal of identifying all popular recipes.