🛰️ Production-ready ML system for geomagnetic storm prediction | 98% AUC, 70% recall | Threshold-optimized ensemble with real-time inference | 29-year dataset (1996-2025) | NOAA SWPC operational standards | Complete MLOps pipeline

Rescuing Moore’s Law via Room-Temperature Soliton Integration in Standard Silicon.

B2B sales lead quality prediction using XGBoost classifier. Achieves 81.06% ROC AUC and 84.74% recall on 7,420 IT sales leads. Handles class imbalance, high-cardinality categoricals, and missing data through frequency encoding and threshold optimization. Includes statistical analysis, cross-validation, feature importance, and business insights.

End-to-end credit card fraud detection using Random Forest and LightGBM, with class imbalance handling, threshold optimization, and SHAP-based model interpretability.

ICU patient mortality prediction using machine learning. The project explores Logistic Regression, KNN, and Random Forest models with focus on handling class imbalance, optimizing recall for mortality detection, and applying threshold tuning for clinical decision support.

Credit risk modeling with HistGradientBoosting, featuring evaluation, SHAP explainability, threshold optimization, and high-risk client analysis.

Built a machine learning model to predict telecom customer churn using classification techniques and SHAP explainability. Optimized performance through tuning and translated results into actionable customer retention insights.

End-to-end supervised ML project predicting Indian cricket team match outcomes using historical data. Covers EDA, feature engineering, Logistic Regression, KNN, Naive Bayes, and Decision Tree (with GridSearchCV tuning) — with actionable BCCI strategy recommendations.

Reproducible credit-default classification pipeline with imbalance-aware evaluation (PR AUC), threshold tuning, and model comparison (LogReg/RF/XGBoost).

Credit default prediction using machine learning with Random Forest and Gradient Boosting, including probability-based error analysis and threshold sensitivity evaluation.

Machine learning–based cardiovascular risk screening app focused on high recall

Visualize binary classifier performance with operating profile plots: score histograms + TPR/FPR/accuracy metrics across all decision thresholds. Python tool for model validation, threshold tuning, ROC analysis, calibration audits

This repository contains a machine learning pipeline for predicting bank marketing campaign success using the Bank Marketing Dataset. It includes data preprocessing, model training (Logistic Regression and Random Forest), and threshold optimization to improve recall for the minority class. The final model is evaluated using precision, recall.

Exploratory financial fraud detection ML pipeline built to study class imbalance, feature behavior, and threshold tradeoffs. Uses XGBoost on a large transaction dataset to analyze recall-precision dynamics, data shortcuts, and system limitations. Educational, not production-ready.

A PM's tool for ML threshold decisions — converts model metrics into daily error costs, support load, and churn impact.

Machine learning system for customer churn risk prioritization using imbalance handling and threshold optimization.

End-to-end credit risk modeling and loan default prediction using LendingClub data

Cost-sensitive customer churn prediction system with threshold optimization and reproducible ML pipeline.

LSTM-based pipeline with dynamic thresholding for detecting anomalies in time series (subset of full FYP)

Build a Gardner Logic-Bridge IP core for threshold-programmable, room-temperature soliton logic in standard silicon