A Python client to interact with Arize API

CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)

A minimal, reproducible explainable-AI demo using SHAP values on tabular data. Trains RandomForest or LogisticRegression models, computes global and local feature importances, and visualizes results through summary and dependence plots, all in under 100 lines of Python.

The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. 📈

A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.

🏦 Build a complete data engineering workflow for a banking system, showcasing ETL processes, data transformations, and an interactive financial dashboard.

A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

Portfolio of real-world ML projects demonstrating ranking & recommendation systems, engagement prediction, fairness, and explainability, engineered end-to-end with scalable, production-ready design principles.

PyTorch implementation of influence functions: ICML 2017 method, TracIn (NeurIPS 2020) and EmpiricalIF (NeurIPS 2022). Estimate how each training sample affects model predictions without retraining.

An application of the WhizML codebase for an analysis of cardiovascular disease risk.

ai powered loan approval prediction system built using machine learning and streamlit. the project analyzes applicant financial data to predict loan approval probability, generate risk scores, provide model insights, and support data driven credit decision making through an interactive analytics dashboard.

End-to-end Credit Risk prediction system — SQL feature engineering, 4-model comparison, Django webapp with SHAP explainability, FastAPI microservice, Docker & Render deployment.

ML-powered property intelligence platform for valuation, locality scoring, growth analysis, negotiation signals, and recommendations.

Business-focused machine learning projects exploring regression, classification, model explainability, and neural networks.

Professional SHAP value computation, analysis, and deployment toolkit for production ML systems

An advanced machine learning library designed to simplify model training, evaluation, and selection.

End-to-end bias audit of healthcare ML models using MEPS dataset. Detects racial disparities, applies 4 mitigation techniques (AIF360), explains predictions (SHAP/LIME), and visualizes findings via Streamlit dashboard.

Credit Scoring model using XGBoost, tracked with MLflow, and explained using SHAP for interpretability.

End-to-end machine learning pipeline for detecting shill bidding fraud in online auctions with model comparison, threshold tuning, and SHAP explainability.

End-to-end ML pipeline for diabetes risk prediction with model comparison, evaluation (ROC-AUC), and SHAP-based explainability.