TrainLens turns training runs into evidence: what changed, what went wrong, and what experiment to run next.

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.

A collection of Kaggle Learn Course Notes for my self learning journey.

A credit risk prediction project that compares multiple machine learning models and applies explainable AI methods to interpret and analyze model decisions.

🚗 Decode car values using a transparent machine learning system that enhances price understanding through explainable methods.