Top 9% solution for the Kaggle March Machine Learning Mania 2026. Optimized ElasticNet, and HistGradientBoosting models blended with betting market insights.
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Updated
Apr 15, 2026 - Jupyter Notebook
Top 9% solution for the Kaggle March Machine Learning Mania 2026. Optimized ElasticNet, and HistGradientBoosting models blended with betting market insights.
Supervised surface-condition classification from torque–angle time series recorded by an automated screwdriving station.
A machine learning web app that predicts a student’s expected score and grade using self-study hours, attendance, and class participation, also showing performance insights such as risk level, focus area, and improvement recommendations. It is built with React, FastAPI, and scikit-learn. Deployment: Vercel for frontend, Render for backend.
Credit risk modeling with HistGradientBoosting, featuring evaluation, SHAP explainability, threshold optimization, and high-risk client analysis.
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