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.

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.

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

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

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.

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

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.

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 data science project focused on predicting the approval likelihood of educational project proposals using text analysis, metadata, statistical insights, and machine learning. The goal is to improve scalability, consistency, and efficiency in proposal evaluation through data-driven decision support.