threshold-optimization

🛰️ 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.

Cost-sensitive loan default prediction using Python and machine learning, with threshold optimization, business cost simulation, model interpretation, and responsible AI considerations.

An end-to-end cost-sensitive customer churn prediction system that combines machine learning, business cost optimization, threshold tuning, and an interactive Streamlit dashboard to prioritize customer retention and reduce potential revenue loss.

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

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.

Data Science project for employee attrition analysis, combining exploratory data analysis, statistical testing, feature selection, machine learning model comparison, hyperparameter tuning and threshold optimization.

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

End-to-end banking churn prediction with cross-validation, threshold optimization, SHAP explainability, customer-value scoring and retention prioritization.

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

Built and deployed an employee attrition prediction application using Scikit-learn and Streamlit. Engineered HR-specific features, applied feature normalization, benchmarked multiple ML algorithms, optimized model hyperparameters and decision thresholds, and integrated the final Logistic Regression pipeline for real-time attrition risk prediction.

Home Credit Risk Prediction (AUC 0.794 / Private LB Top 15%)

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.

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.

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

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

Machine Learning and Power BI based Credit Card Fraud Detection system with dynamic threshold analysis, ROC/PR evaluation, confusion matrix visualization, feature importance analysis, and business impact analytics using multiple ML models including XGBoost, Random Forest, ANN, and Logistic Regression.

End-to-end predictive maintenance ML project using the AI4I 2020 dataset, with feature engineering, model tuning, threshold optimization, cost-sensitive evaluation, interpretability, error analysis, CLI execution and reproducible reporting.

📉 End-to-end telecom churn ML system on 7,043 records SQL analytics, 8 models, Optuna tuning, SHAP explainability, and a cost-sensitive threshold sweep recovering $84K/yr · 0.847 AUC · FastAPI + Dash.

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.