Forecasted redemption and subscription pattern of retail investors in equity mutual funds by analyzing portfolios with AUM of above $ 0.5 Trillion for advantages in liquidity risk management and assessing investor sentiment Analyzed the impact of fund performance, sector, macroeconomic factors, fund fees, domicile, total net assets.Applied machine learning and econometric techniques like random forests, pooled OLS estimation

Simulated and tested various file discovery protocols in static and dynamic P2P Mobile Adhoc networks. Optimized file transfer algorithms for lesser redundancy and better network latency and bandwidth

Performed ETL and created a random forest model with PySpark to predict student performance.Built an asynchronous pipeline using RabbitMQ and used Redis for low latency data consumption.Deployed the model as a containerized Flask application using docker in a microservice architecture