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.
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.
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.
🤖 Machine Learning Model Explainer for InterSystems Cloud SQL | 📊 Create, train, validate & predict ML models with interactive explainability dashboards featuring SHAP values, feature importance, partial dependence plots, model performance metrics, and decision tree visualizations.
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.
High-performance interpretable ML classifier using High Utility Gain patterns (IEEE Access 2024). C++ accelerated, scikit-learn compatible, with EBM-style explanations, adaptive binning, pattern pruning, and deployment tooling for regulated domains.
An application of the WhizML codebase for an analysis of cardiovascular disease risk.
End-to-end Credit Risk prediction system — SQL feature engineering, 4-model comparison, Django webapp with SHAP explainability, FastAPI microservice, Docker & Render deployment.
Synthetic FHIR + FastAPI ML service for 30-day hospital readmission risk scoring with explainability, Docker, tests, and CI-ready MLOps patterns.
ML-powered property intelligence platform for valuation, locality scoring, growth analysis, negotiation signals, and recommendations.
Professional SHAP value computation, analysis, and deployment toolkit for production ML systems
Interactive SHAP explainability dashboard for tabular machine learning models using scikit-learn and Streamlit.
Credit Scoring model using XGBoost, tracked with MLflow, and explained using SHAP for interpretability.
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.
An advanced machine learning library designed to simplify model training, evaluation, and selection.
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