AI-powered medical imaging system for multi-disease chest X-ray detection,built with EfficientNet deep learning, a FastAPI backend, and an interactive Streamlit dashboard. Deployed on Render for real-time healthcare diagnostics, detecting conditions like Atelectasis, Edema and more.An end-to-end project demonstrating model training,API development.

An end-to-end regression project trained on the Boston Housing dataset, featuring data analysis, Random Forest modeling, model evaluation, and real-time prediction through a REST API built with FastAPI.

An end-to-end MLOps project using Python, GitHub Actions, and Evidently AI to automate model monitoring, drift detection, and retraining.

End-to-end ML pipeline for predicting credit card payment default using XGBoost, SHAP explainability, and a Streamlit based UI

Production-ready ML model predicting DoorDash delivery times.

Machine learning project for predicting vehicle CO₂ emissions (g/km)

A Python-based handwritten character recognition system that uses a CNN trained on self-collected data to accurately recognize mouse-drawn characters. Includes a unified desktop UI for real-time prediction, dataset management, and model retraining.

End-to-end MLOps Facial Emotion Recognition app with ViT, Gradio UI, and CI/CD deployment on Hugging Face Spaces.

This project builds a predictive model to estimate visa approval likelihood using candidate and job-related features. It showcases an end-to-end machine learning workflow with EDA, feature engineering, and model tuning to automate parts of the visa evaluation process.

An end-to-end machine learning project to predict the sale price of bulldozers. This repository details a full data science workflow, including data preprocessing, model training with scikit-learn pipelines, hyperparameter tuning, and model evaluation.

Sentiment Analysis is a Natural Language Processing (NLP) technique used to identify the emotional tone behind a piece of text — typically classified as positive, negative, or neutral.

✈️ End-to-end ML web app that predicts Indian domestic flight ticket prices. Built with Python, scikit-learn & Flask — covers data cleaning, feature engineering (34 features from 10K+ records), model comparison (Lasso, Ridge, SVR & more), and a responsive UI for real-time predictions.

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.

This project delivers a seamless recommendation experience by blending machine learning similarity models with The Movie Database (TMDB) API for real-time posters, summaries, and release details. Lightweight, fast, and designed for production-grade deployment.

Production-ready customer segmentation system with interactive Streamlit dashboard, FastAPI REST API, and SHAP explainability

Production-grade end-to-end MLOps pipeline for vehicle insurance response prediction, built with scikit-learn, FastAPI, Docker, and AWS. Covers the full ML lifecycle: MongoDB-based data ingestion, schema validation, feature engineering, model training and evaluation, model registry on S3, CI/CD with GitHub Actions, and cloud deployment on EC2.

Implements the full ML lifecycle: time-aware feature engineering, XGBoost modeling with Optuna + MLflow, FastAPI inference, Streamlit dashboard, Dockerized services, CI/CD via GitHub Actions, and deployment on AWS ECS Fargate (S3, ECR, ALB).

ML regression project predicting hyperlocal delivery times using distance, traffic, weather, and driver features. Includes data generation, EDA, training pipeline, and Streamlit deployment.

End-to-end machine learning project with leakage-aware modeling and business-driven threshold optimization.