shap-explainability

Autonomous AI Predictive Analytics Platform

MultiModal Disaster Response System — BERT + ResNet-50 fusion for real-time disaster intelligence

Spatio-temporal graph deep learning for IPL T20 match outcome prediction. GAT player-interaction graph + BiLSTM + cross-attention Transformer. Ball-by-ball win probability, run forecasting & Player Impact Score with SHAP explainability.

End-to-end customer churn prediction project using the Telco dataset. Includes EDA, data preprocessing, Logistic Regression / Random Forest / XGBoost model comparison, SHAP explainability, and a production-ready prediction pipeline.

Credit risk prediction using machine learning, ensemble modeling, Optuna optimization, SHAP explainability, fairness analysis, and interactive deployment.

This project employs Logistic Regression for binary classification, to predict whether a borrower is capable of repaying a loan based on various financial and demographic factors.

ML-powered crop yield prediction for Maharashtra — Gradient Boosting (R²=0.977), SHAP explainability, MLflow experiment tracking, LLM-powered AI explanations, and interactive Streamlit dashboard.

Enterprise-grade Android threat intelligence platform combining static APK analysis (Androguard), dynamic sandboxing (Frida), LLM-powered behavioral assessment (Gemini 2.0), and explainable ML (XGBoost + SHAP). Features real-time streaming analysis, India-specific banking trojan detection, multi-dimensional risk scoring, and compliance-ready audit

Real-time PPG-based atrial fibrillation detection using Random Forest with feature-level explainability and interactive visualization.

Multi-module clinical ML framework for real-time ICU mortality prediction, multimorbidity risk scoring, and waveform event detection with automated MLOps drift monitoring and ensemble fusion.

LangGraph-based agent pipeline for code evaluation using static analysis, FAISS-powered RAG, and LLMs with SHAP-style explainability.

A Clinical Decision Support System (CDSS) for Diabetes Risk using Agentic AI & SHAP Explainability. Developed for Cotiviti Topic 2: Prediction, Pattern Recognition, and Agentic Generative AI for TPO.

The AI Loan Analyst is a sophisticated Streamlit-based web application designed to automate and enhance the loan analysis process for financial institutions. It combines data science, machine learning, and financial modeling to provide a complete loan portfolio management solution.

I built this application to allow users to input various clinical parameters and receive an instant prediction of whether a patient is likely to have CKD. The app is hosted on Streamlit community cloud for public access.

ML-based beta thalassemia carrier screening from CBC parameters for Sri Lankan primary care. SVM + SHAP explainability, MOH referral letters, couple screening, voice input, and analytics dashboard.

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.

Production-oriented credit card fraud detection system — tuned XGBoost (PR-AUC 0.85) vs. SMOTE, Isolation Forest, and AutoEncoder baselines, SHAP explainability, FastAPI serving, SQL-backed pipelines, PSI drift + latency monitoring, structured JSON logging, and CI-tested components.