- Mid-frequency alpha research using technical indicators- Boosting high frequency model performance using mid-frequency alphas

- Designed dashboards to monitor the financial health of banking institutions and compare them to RBI-recommended values- Wrote Python scripts to implement pipelines to get the required data for the dashboards from the periodic data published by the banking institution

-Worked on CNC tool wear classification prediction model Prof. Alkan Burga.-Did literature review of more than 50 research papers in the field and developed a working knowledge in the field of intelligent manufacturing.-Coded and backtested various deep learning models on the CNC milling dataset by University of Michigan.-Developed a Random Forest based approach for tool wear prediction on the same dataset with an accuracy of 94.4% and wrote a research paper for the same.

1) Web scraping company data - Scraped data of 50,000 companies from zaubacorp. - Data of each company contained the names of the directors and their DIN codes. - Clustered companies with common directors. 2) Predicting NPA of banks during forbearance - The utilised financial data of companies from Prowess and default data from CIBIL to make a machine learning model that is trained in the period of 2002-2008. - The model predicts bank defaults in the period of 2009-2015 and MCA data is used to calculate the NPA percentage of individual banks in a particular year. - A ran a regression between NPA reported and, NPA calculated against the change in stock prices of a bank to find that the more the banks were hiding NPA, the better their stock performed.

- Built a Java Spring Boot application for creating a database of influencer profiles on YouTube using Selenium and YouTube Data API- Created RESTful APIs to access the data from the database and request the service for new data by triggering different kinds of jobs- Implemented functionality to persist the collected data in a PostgreSQL database using the Data Access Object (DAO) pattern

-Analysed stocks using technical and fundamental analysis according to the client requirements and made stock reports for the same.-Applied various trading strategies in python like Pitroski F score, momentum, pairs trading and backtested them with historical stock market data.