End-to-end ML pipeline for UCI Heart Disease classification. Includes leak-safe preprocessing, baseline + Random Forest + HistGradientBoosting, val-tuned thresholds, and CI that generates a downloadable reports artifact. Best model (HGB) hits F1=0.872, Acc=0.891 on the held-out test set

End-to-end fraud detection pipeline with imbalanced data, probability-based evaluation, threshold tuning, and business-driven model selection using Logistic Regression, Random Forest, and XGBoost.

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

Identifying rare event.

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

End-to-end credit card fraud detection using Machine Learning and Threshold tuning.

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

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.

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.