IMDB reviews sentiment analysis: EDA → TF-IDF baselines (NB/LogReg/Linear SVM + calibration) → F1 threshold tuning → explainability → BiLSTM baseline.

Production-minded Streamlit + Plotly fraud detection dashboard with decision policies (Strict/Balanced/Lenient), cost-vs-threshold analysis, and calibrated model artifacts.

SMS spam detection pipeline: dual TF-IDF (word+char) → calibrated Linear SVM, nested CV + threshold tuning (F1) + explainability + robustness tests.

Advanced Streamlit + Plotly sentiment analysis lab: TF-IDF (word+char), multi-model training, ROC/PR AUC evaluation, cost-aware threshold tuning, error analysis, and live prediction.

Digital habits → wellbeing risk (binary): calibrated risk scoring + cost-aware thresholding with deployable artifacts (LogReg/RF/XGBoost).

Decision-grade donor outreach policy: calibrated P(donated_next_6m) scoring + budgeted Top-K actions + net-benefit optimal threshold, with exported deployable artifacts.

12h clinical deterioration early-warning baseline: tabular models (LogReg / HistGradientBoosting / optional XGBoost), simple probability ensemble, and cost-based threshold/policy tables exported for dashboards.

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.

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

Worked on a Fraud detection supervised machine learning case study and built a fraud detection end-to-end model along with a risk framework thhrough root cause analysis and cost-benefit threshold tuning to improve fraud identification and decisioning.

Identifying rare event.

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

Predicting bank term deposit subscriptions using Logistic Regression with recall optimization and threshold tuning.

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

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.

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.