Follow your heart with a rational mind. Contact: rravichandran@ucdavis.edu
Programming and Analysis Tools : Python, R, SQL, Pandas, NumPy, Statsmodels, Scikit-learn, PyTorch, TensorFlow
Data Management and Visualization : MySQL, PostgreSQL, Timescale, MongoDB, Elastisearch, Spark, Kafka, Grafana, Tableau
Infrastructure and Operations: Docker, Airflow, AWS, Azure, GCP, Databricks, Git, Unix (Shell), Jira, MLFlow
M.S., Business Analytics | University of California, Davis (June 2024) |
B.Tech., Mechanical Engineering | SASTRA Deemed University (Aug 2019) |
Data Scientist @ Practicum Project at a Leading B2B Lender, USA (Sept 2023 - Present)
Data Science Engineer @ Vunet Systems, India (Oct 2020 - June 2023)
Research Intern @ Deakin University, Australia (Jan 2019 - July 2019)
This study explores that with COVID-19, our attitudes toward physical contact could have shifted, affecting our high-five choices. Our experiment shows how small visual cues can shape social behavior, with air high fives remaining popular amid ongoing concerns about health and safety. Also it explores if the gender of someone holding a cardboard sign influences people’s preference for physical or air high fives. On high level, we used block experimental design, performed hypothesis and contingency tests to explore the significance of physical interactions which could further be related and significant to business, and other promotional activities.
This project means a lot to me. When I was watching a Liverpool game in New York, I started thinking about how well fans could guess the outcome. I wanted to look into this further by checking how fans felt before big football matches and using that to guess who would win. Instead of using old player or match stats, I believe I could use the fans sentiments which I consider to be super fun instrumental variable. So the idea is to develop sentiment models using NLP techniques, using data from public soccer forums to predict match outcomes based on online sentiment polarities, which has the potential to help in making informed betting decisions by capturing the latest trends before the game.
Finalist in the hackathon organized by City and County of San Francisco, analyzed San Francisco traffic accidents data and engineered features for a logistic regression model, enhancing Vision Zero initiatives with actionable, data-driven policy recommendations.
You can look for other projects in my github :)