AI/ML Engineer Portfolio

Python | Scikit-learn | PyTorch | Hugging Face | RAG | Docker

Open to mid-level AI/ML roles (India & Japan)
Each project has its own pinned requirements.txt — see individual folders for exact dependencies and how to run.

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✅ Completed Projects

1. Japanese RAG Production System

Status: ✅ COMPLETED & PORTFOLIO-READY

Key Achievements

• Built a modular production-oriented RAG pipeline specialized for Japanese documents
• Implemented real RAGAS evaluation (Faithfulness 0.96, Answer Relevancy 0.76) using actual system outputs
• Developed FastAPI backend + Streamlit frontend with clear separation of concerns
• Japanese-aware chunking + bge-m3 embeddings
• Full Docker support + professional documentation

Repository: Japanese_RAG_Production

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2. Credit Card Fraud Detection

Status: ✅ COMPLETED & PRODUCTION-READY

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
• Business insights included

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

Key Achievements

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

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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4. Customer Churn Prediction

Status: ✅ COMPLETED & LIVE

Key Achievements

• End-to-end ML pipeline with production-ready artifact
• Interactive Streamlit web application
• Strong business insights and documentation
• Accuracy 0.82 | Recall 0.57 (priority metric)

Live Demo: Streamlit App

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All projects follow PEP8 standards, modular structure, and pinned dependencies.
Every project includes clear documentation and 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、日本語はN4レベル（N3勉強中）。初回面談は英語メインで対応可能。

特に日本語文書向けRAGシステム、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。