Earnomly is a modern fintech-styled platform that uses machine learning (Random Forest, Logistic Regression) to analyze socioeconomic factors and predict income levels using the UCI Adult dataset.

An analysis of the factors that explain real estate prices in Tokyo.

This project leverages machine learning techniques to analyze online retail data through customer segmentation. It uses KMeans clustering to identify key customer groups and proposes tailored business strategies based on their purchasing behaviors.

Data Analysis with Python Projects leverage the power of Python's extensive libraries and tools to explore, visualize, and draw insights from datasets

This project predicts London’s daily temperature using machine learning. It follows a structured ML workflow with EDA, data cleaning, model training, and experiment tracking with MLflow.

FailSafe: Autonomous Deployment Risk Engine

Transfer learning-based deep learning pipeline using DenseNet121 for automated pneumonia detection from chest X-ray images. The project includes data augmentation, model optimization, evaluation via confusion matrix, and a production-ready training pipeline.

End-to-End Machine Learning web app featuring automated CI/CD pipelines and cloud deployment on AWS

SegmentStream Test App

PacketWatch – Advanced Network Intrusion Detection using Machine Learning. Real-time monitoring, anomaly detection, and secure API deployment with FastAPI

Production-style ML classification service with configurable training, MLflow, FastAPI inference, drift checks, monitoring, Docker, and CI.

Unsupervised clustering model that segments German credit customers by demographic and behavioral attributes to support targeted decision-making.

Machine Learning

AL/ML Internship Tasks

An end-to-end MLOps pipeline for 3-day Air Quality (PM2.5) forecasting. Integrates Open-Meteo weather data, Hopsworks feature store, and automated GitHub Actions to train and deploy multi-step ML models.