End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.

Solutions and notebooks for data mining and machine learning competitions. Feature engineering, model stacking, hyperparameter tuning, and ensemble techniques.

Personal data science portfolio and blog showcasing projects in machine learning, data visualization, AI agents, web scraping, and statistical analysis. Built with Bootstrap, Plotly, and vanilla JS.

Movie recommendation system using collaborative filtering and content-based approaches. Analysis of viewing patterns and genre preferences.

Sports score prediction using statistical models and machine learning. Time series analysis, Poisson regression, and ensemble methods for match outcome forecasting.

Health data analysis and personal wellness tracking. Statistical modeling of fitness metrics, sleep patterns, and nutrition data with interactive visualizations.