Thanks for stopping by! Here you can find some of my academic and personal projects.
This project was completed in collaboration with Natasha Vij for our MS in Data Science Capstone Project at The George Washington Univeristy. The goals of this project were to:
This project explores trends and commonalities among aviation crashes using unique and meaningful visualizations. The data used for this analysis takes into account civil, commercial, military, and cargo aircrafts.
Specifically, this project will look into trends in the following categories:
Machine learning project done by Sabina Azim, Arushi Kapoor, and Natasha Vij. The topic for this project was obtained from the annual Women in Data Science Datathon organized on Kaggle. The purpose of this challenge is to “determine whether a patient admitted to an ICU has been diagnosed with a particular type of diabetes, Diabetes Mellitus.”
Comparing the military, healthcare, and education expenditure of various nations in the G20. Specifically looking at the total overall spending in each of these departments, as well as in relation to each country’s GDP and population over the five year period of 2013-2017. This project utilizes the Google Visualization API, HTML, and CSS.
Using the Snapchat Political Ads Library which Snapchat has made publicly available in 2018 to look at what political organizations use Snapchat the most for advertising, which ads make the most impressions, targeted age/geographic groups, and what kinds of ads were more prevalent in 2020 around election time.
Analysis of the happiest and least happiest countries from 2016-2020, what factors weigh heaviest when it comes to calculating the Happiness Score, and if a country’s Happiness Ranking has any correlation to their suicide mortality rate.
Introduction to Data Science final group project done by Sabina Azim, Harish Ram, and Kristin Levine. We used various Data Science methodologies and algorithms to make recommendations to someone who is planning to build a new electric vehicle charging station. What insights can we give them to optimize their profit and success?
Practiced using the K-Nearest Neighbors to classify university as Private or Non-Private based on various features.