I did my Masters in Data Analytics with specialiation in Computation Analytics from Georgia Tech. I got the opportunity to work with wonderful team mates, courses and great projects. Sharing details of some projects that I worked on.
In this project our goal is to identify patterns and clusters in accidents across the U.S. using the dataset which contains data on more than two million vehicle accidents, over a 3-year period, for the entire contiguous United States, and includes variables related to weather, lighting/time of day, and road environment.
I will be using a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. For more information about this dataset, please visit here: https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents For the project analysis, I am using the sampled version, which includes 500,000 accidents this is also specified in the above link. And 46 features extracted from the original data set for easier handling and analysis. I will be performing the necessary EDA on the data, after which will look to execute different Machine Learning clustering algorithms to look at the patterns in the data.
In the dynamic world of investment, the concept of thematic investing has taken center stage, capturing the interest of modern investors who seek not only to diversify their portfolios but also to align their investments with specific areas projected to yield above-market returns over the long term. This project is rooted in the pressing need to enhance the efficiency, timeliness, and objectivity in the identification and evaluation of emerging market themes, thereby offering investors a golden opportunity to capitalize on these trends at their nascent stages for maximized return potential.
we aim to develop a computational approach of leveraging annual SEC filings to identify emerging “themes” in the stock market.1 By employing Natural Language Processing (NLP) techniques to analyze textual data within SEC filings, we seek to derive noteworthy themes, identify key phrases that are indicative of each company or industry’s trajectory over time, and recognize patterns in these trends that signal new opportunities.