1)Used LLMs to develop customized sales bullet points for representatives to market products to its users 2)Trained NLP models from scratch using Tensorflow to predict customer churn3)Improved the model metric scores by 6.7% and captured an extra 676 churners

1) Severity segmentation of patients with cancer: - Developed a Decision Tree-based Classification Model to segment patients based on clinical attributes & guide treatment decisions for clinicians. 2) Cancer growth prediction:- Evaluated multiple 3D Convolutional Neural Network Models (ResNet, AlexNet) using 3D CT scans to predict brain tumor growth post radiation treatment.- Reduced model training time by 90% through image compression techniques & using multi-GPU training.- Trained an XGBoost & Random Forest Models on clinical & imaging features- Increased AUC by 7.6% by combining imaging & clinical data using a Stacked Ensemble Model.

- Performed data processing on insurance claims using Apache Spark & Hive for market analysis.- Developed an automated ETL pipeline for collecting raw data from vendors and ingesting into Hive tables. - Segmented hospitals by degree of drug penetration to identify key variables using Decision Tree Models. - Designed a Tableau Dashboard to track COVID-19 Statistics by US States to derive actionable insights for clients.

- Prepared an excel workbook to track inventory levels and predicted sales of clothing items.

-Tracked Supply planning KPI's using excel and performed a root cause analysis of below target KPI's.