Build ML pipelines you can trust — visually on a canvas or in Python. Self-hosted platform + Apache-2.0 library. Catches data leakage, keeps scores honest, and takes you from raw data to monitored model.
An automated pipeline that uses the YouTube Data API to extract video data based on specific keywords, storing the most-viewed videos in GitHub for trend analysis and actionable insights.
End-to-end data preprocessing pipeline using the IBM HR Employee Attrition dataset with Python, Pandas, and Google Colab.
AI-powered data preparation pipeline: upload messy files, review AI cleaning plans, download analysis-ready datasets. Built on Azure AI Foundry + 8-agent orchestration.
9段階のイテレーションを通じた心臓病予測の進化。統計学（標本誤差の最小化）を実戦投入し、5-Seed Averagingによる極めて高い汎化性能と実務的信頼性を実現したMLポートフォリオ。
A machine learning project on the Ames Housing dataset covering data preprocessing, exploratory data analysis (EDA), feature engineering, model building, and evaluation.
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