NBA Statistics Scraper

I built a web scraper that gathered statistical information from every active player in the NBA in the 2016-2017 season, as well as offensive and defensive team stats for each team. The data was pulled, cleaned, and displayed using a combination of the python libraries Numpy, Pandas, Bokeh, and BeautifulSoup. Hover over the graph to see some statistic. The legend in the bottom right is also interactive, click on a position to try it out!

Moving forward, I would like to analyze the data that I compiled and create a machine learning model that can predict roughly how well a player is going to do based on recent performances, offensive/defensive team metrics, and other statistics.

-- Link to Github --

About Me

I am a computer science and cognitive science double major at Swarthmore College graduating in the Spring of 2020.
In addition, I am also fascinated by psychology, neuroscience, and nutrition. In my free time, I like to play baksetball and workout, cook, make/listen to music, and go chasing sunsets. I play many instruments, but mainly the piano and GuZheng. My favorite classical pieces include Grades etudes de Paganini by Franz Liszt, all three of Rachmaninioff's Piano Concertos, and Chopin Scherzo No.3 in C Sharp Minor, Op.39.

In computer science, I am passionate about parallel and distributed systems and machine learning applications. In cognitive science, I am fascinated by the microbiome of the gut, the psychology of language, as well as physiological psychology.

On a journey to quench my thirst for knowledge