A comprehensive portfolio showcasing expertise in machine learning, data science, and healthcare AI. Each project demonstrates end-to-end development from data processing to production deployment.
Healthcare & AI Research Projects
Suicide Prevention Survey Platform
Purpose: Clinical decision support system for Greek hospitals enabling doctors to assess patient suicide risk through validated screening tools.
Technical Implementation: Built with Streamlit for rapid deployment, integrated with PostgreSQL for secure data storage, and featuring real-time risk scoring algorithms. Implements GDPR-compliant data handling and encrypted patient information storage.
Impact: Deployed in pilot hospitals, supporting mental health professionals in early intervention and risk assessment.
Tech Stack: Python, Streamlit, PostgreSQL, Pandas, Plotly, Security Encryption
COVID-19 Genetic Factor Classification
Purpose: Machine learning system for COVID-19 diagnosis based solely on genetic factors, supporting clinical decision-making when traditional testing is unavailable.
ML Pipeline: Feature engineering on genetic markers, ensemble models (Random Forest, XGBoost, LightGBM), SHAP values for model interpretability, and cross-validation for robust performance.
Results: 87% accuracy in predicting COVID-19 status from genetic factors, with detailed feature importance analysis identifying key genetic markers.
Tech Stack: Python, Scikit-learn, XGBoost, SHAP, Pandas, NumPy, Seaborn
Data Science & Automation Projects
Habit Tracker
Purpose: Personal productivity application for tracking daily habits and building consistent routines.
Features: Daily habit logging, progress visualization, streak tracking, customizable habit categories, and data persistence for long-term progress monitoring.
Implementation: Built with modern web technologies, featuring an intuitive interface for quick daily check-ins and comprehensive analytics to visualize habit consistency over time.
Tech Stack: React 18 + TypeScript, Vite, Tailwind CSS, Supabase (database/auth), Capacitor (iOS/Android), Vite PWA, Recharts, Framer Motion.
Gmail Newsletter Reporter
Purpose: Automated email intelligence system integrating multiple Google APIs and OpenAI for smart email summarization and reporting.
Features: Gmail API integration for email extraction, OpenAI GPT for intelligent summarization, automatic Google Docs report generation, Telegram notifications, and CLI configuration management.
Architecture: Modular Python design with async processing, configurable pipelines, and scheduled automation via cron jobs.
Tech Stack: Python, Gmail API, Google Sheets API, Google Docs API, OpenAI API, Telegram Bot API, AsyncIO, Click (CLI)
Polls Analyzer - Interactive Survey Platform
Purpose: Full-stack survey creation and analysis platform with real-time visualization and statistical insights.
Features: Dynamic poll creation interface, real-time response collection, interactive charts (Plotly), statistical analysis (Chi-square, correlation), and exportable reports.
Analytics: Response distribution analysis, demographic segmentation, trend identification, and automated insight generation.
Tech Stack: Python, Streamlit, Plotly, Pandas, NumPy, SciPy, SQLite
ML Feature Engineering FastAPI Service
Purpose: Production-ready microservice for automated feature engineering and ML preprocessing at scale.
Architecture: FastAPI for high-performance async endpoints, Docker Compose for containerization, Uvicorn for ASGI server, and comprehensive API documentation with Swagger UI.
Features: Automated feature generation, missing value imputation, categorical encoding, feature scaling, and feature selection algorithms. RESTful API design with input validation and error handling.
Performance: Handles 1000+ requests/second, horizontal scaling via Docker Swarm, and production-ready logging and monitoring.
Tech Stack: Python, FastAPI, Docker, Uvicorn, Scikit-learn, Pandas, Pydantic, Pytest
Media & Personal Projects
Bonobo Gentlemen Podcast
About: A podcast series exploring the intersection of technology, personal development, and life philosophy. Episodes feature in-depth discussions on machine learning, career development, mental health, and productivity.
Topics: AI/ML industry insights, career transitions in tech, work-life balance, mental health awareness, productivity systems, and continuous learning strategies.
Production: Audio editing with Audacity, hosting on Spotify and Apple Podcasts, and social media promotion strategy.