Welcome to my collection of end-to-end machine learning projects! This repository showcases a variety of real-world ML use cases, each built with clean, modular code, reproducible pipelines, and production-ready practices. Projects include everything from classification and regression to time-series forecasting and recommendation systems.
Key highlights:
- 📦 Structured codebase for each project (data, models, scripts, src, tests)
- ⚙️ Feature engineering pipelines and preprocessing logic
- 📊 Integrated model tracking and evaluation using MLflow
- 🔁 Support for multiple models (e.g., Logistic Regression, Random Forest, XGBoost)
- 📈 Visualizations, metrics logging, and artifact storage
- 🧪 Unit test support and configurable workflows
Perfect for learning, showcasing your portfolio, or adapting into production-ready solutions.