A python library to send data to Arize AI!
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.
An application of the WhizML codebase for an analysis of cardiovascular disease risk.
Credit Scoring model using XGBoost, tracked with MLflow, and explained using SHAP for interpretability.
An advanced machine learning library designed to simplify model training, evaluation, and selection.
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
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.
Next-generation automated machine learning that not only trains models but also explains them, optimizes hyperparameters, and generates deployment code.
End-to-end retail sales forecasting using LightGBM with time-series features, SHAP explainability, FastAPI inference, Streamlit demo, and CI for production-ready ML workflows.
Easy-to-use UI based tool that visualizes the internal layers and activations of any Pytorch network that takes image as input , built using PyQt
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