Machine Learning Systems

Contains Colab Notebooks show cool use-cases of different GCP ML APIs.

A scalable demand forecasting system implementing automated retraining, MLflow-based model management, and cloud deployment using production-grade MLOps practices.

Hybrid fraud detection and mass defect monitoring system built with BigQuery ML. Combines Logistic Regression and KMeans anomaly detection to identify return fraud, emerging risks, and product quality issues using a scalable cloud-native analytics pipeline.

CS2 Skin Preview & Customization Utility for Weapons and Inventory is a visual tool for exploring and customizing weapon and inventory appearances in Counter-Strike 2, designed for previews, loadout styling, and cosmetic experimentation.

Detect U.S. housing market bubbles using macroeconomic signals. Forecast HPI, score speculative risk, and visualize insights using a fully modular, cloud-native GCP pipeline.

Production-grade customer segmentation pipeline built on Azure (Blob Storage, Data Factory, Azure ML, Batch Endpoint). Includes end-to-end data engineering, feature engineering, K-Means model training, and scalable batch inference.

Production-ready demand forecasting API using time-series features, FastAPI, Docker, and Google Cloud Run.

Leveraged Random Forest Regression and Forecast models to analyze my sleep data from the past two years.

Detect return fraud and product defects in retail using BigQuery ML with supervised and unsupervised machine learning methods.