At Cognizant, I built an end-to-end Azure Data Factory pipeline using Medallion Architecture to process JSON, CSV, and XML data across Azure Data Lake Storage Gen2 and Azure SQL Database. I developed Mapping Data Flows and SQL stored procedures for cleansing, validation, SCD Type 2 historization, and fact table loading.
At CollegeTips, I cleaned and structured more than 15 datasets, reducing data preparation and analysis time by approximately 30%. I also built more than 10 dashboards and visualizations to track campaign performance.
During my Machine Learning Internship at Jadavpur University, I developed XGBoost classifiers for three Alzheimer’s disease stage classification tasks using MRI-derived features from ADNI scans. I applied z-test based feature selection, achieving maximum accuracies of 94.58%, 85.97%, and 86.60% across the three tasks.
My projects include JobShield, a Streamlit app for resume parsing and job-safety analysis, and StockSense, an XGBoost-based SKU demand forecasting application. I also built an exam seating and invigilator assignment web application using React, Flask, and SQLite.
