This project builds a complete ML pipeline to model medical insurance costs based on demographic and lifestyle features. The workflow includes exploratory data analysis (EDA), preprocessing, multiple regression models, hyperparameter tuning, ensemble stacking, log-transform modeling, SHAP interpretability, and full residual diagnostics.

Leakage-aware flood susceptibility mapping using classical machine learning and strict spatial holdout, with reproducible preprocessing, explainability analysis, and benchmark comparison across eight models.

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

The online payment fraud analysis project follows several step approach from data preprocessing through model evaluation, result comparison and final model selection, using transaction patterns to identify fraud indicators including account draining, suspicious transfers, and balance inconsistencies.

MoneyLion DS Assessment