A complete end-to-end machine learning pipeline to predict employee salaries (USD) using job-related features. Includes automated EDA, robust preprocessing, ML model training with MLflow, drift detection with Evidently AI, and a Flask-based web app for both single and batch predictions.

Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.

The "Insurance Claims MLOps Lifecycle Automated Pipeline" GitHub project offers an efficient solution for insurance claim processing. Leveraging Azure services, it covers data engineering, model development, MLOps integration, deployment, and application. Automated pipelines streamline workflows, ensuring robust, scalable production environments.

Fine-tuned roberta-base classifier on the LIAR dataset. Aaccepts multiple input types text, URLs, and PDFs and outputs a prediction with a confidence score. It also leverages google/flan-t5-base to generate explanations and uses an Agentic AI with LangGraph to orchestrate agents for planning, retrieval, execution, fallback, and reasoning.

An end-to-end machine learning web app that classifies PDF resumes into job-fit categories. Built with FastAPI, Streamlit & Docker. Deployed on Render.