ML-Projects | CodeForge Portfolio

AI/ML Engineer Portfolio
Python | Scikit-learn | PyTorch | Hugging Face
Targeting mid-level roles in Japan (Human Resocia / Pasona / BizReach)

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✅ Completed Projects

1. Customer Churn Prediction

Status: ✅ Completed & Live
Duration: 2 days | Tech: Scikit-learn, Streamlit, Pandas

• End-to-end ML pipeline with production-ready artifact
• Interactive Streamlit web application
• Strong business insights and detailed Japanese documentation

Live Demo: Streamlit App

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2. Credit Card Fraud Detection

Status: ✅ COMPLETED & PRODUCTION-READY
Duration: 7 days (Docker sprint)

Key Achievements

• High-recall XGBoost (recall 0.92, PR-AUC 0.85)
• SHAP explainability (V14/V17 main drivers)
• Docker container + Streamlit live demo
• Unit tests + pinned dependencies
• Japanese summary + business insights

Live Demo: Run docker compose up fraud-web → http://localhost:8501

Live Demo (Streamlit Cloud): [https://ml-projects-credit-card-fraud-detection.streamlit.app/]

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3. Japanese Sentiment Analysis (NLP)

Status: ✅ COMPLETED & PORTFOLIO-READY
Duration: 5 days

Key Achievements

• Fine-tuned Japanese BERT (cl-tohoku/bert-base-japanese-v2) with 3-class sentiment
• Production deployment on Gradio (public link) + Streamlit Cloud (CPU-optimized)
• Model pushed to Hugging Face Hub (Retro099/japanese-sentiment-analysis-v1)
• Professional assets: confusion matrix, Japanese summary, requirements_nlp.txt

Live Demo: Gradio → https://f50c787d7b105f7bf9.gradio.live/
Streamlit Cloud: [https://cx7v54eehcppwnarlaplxt.streamlit.app/]
Model on HF Hub: https://huggingface.co/Retro099/japanese-sentiment-analysis-v1

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All projects follow PEP8 standards, modular structure, and pinned dependencies.
Every project includes a Japanese summary and clear business impact section.

日本就業に向けたポートフォリオ概要
日本でのデータサイエンティスト / MLエンジニア就業を目指してポートフォリオを強化中。在留資格取得手続き中、2026年6月にJLPT N3受験予定。初回面談は英語メインで対応可能。Docker本番運用・SHAP説明性・日本語BERTファインチューニングを強みとするプロジェクト群です。