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

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

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

End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.

Production-ready ML model predicting DoorDash delivery times.

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.

An end-to-end machine learning project built on the UCI Heart Disease dataset, covering data preprocessing, feature engineering, model training, evaluation, and deployment. The project includes Streamlit app that supports both single-patient and batch predictions, ensuring reproducibility through a well-structured pipeline and saved model artifacts

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.

✈️ 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-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.

End-to-end ML project: 30-day hospital readmission prediction using XGBoost, PostgreSQL, Python and Tableau

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

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).