Created NBA stat that values players based onthe context of their teammates. Used python and sql to get, store, compute, and display a variety of player and team info related to Wins Contributed including creating supervised and unsupervised learning models to predict similar players and team wins for the upcoming season.
- Supervised ML
- Unsupervised ML
- SQL
- Python
- Matplotlib
- Scikit Learn
- Pandas
- Numpy
Built this website! This iteration uses React for front end but previous iterations used Flask as the server and a python front end
- React
- HTML
- CSS
- Javascript
- Material Ui
- Flask
- Heroku
A bot that scrapes data and predicts an optimal daily fantasy lineup with the goal of winning 1 on 1 contests greater than 57% of the time. Used selenium to scrape data, python to store and analyze data, and machine learning to predict the best lineup.
- Selenium
- Python
- Time Series Analysis
- MongoDB
- XGBoost
- Pandas
- Numpy
Explored the history of the United States through the lens of presidential speeches. Uses many AWS nodes to scrape data quickly; NLU techniques and data visualization to display insights from speeches; and Unsuperivsed ML to create similarity clusters for Presidents
- AWS
- Matplotlib
- Python
- Seaborn
- SQL
- NLTK
- Tensorflow
- Spacy
- Plotly
- SciKit Learn
- BeautifulSoup
A group project from the data science bootcamp where we built an application that leveraged a self-made fraud model to predict the likelihood of fraud in a transaction. We used Flask to create the front end and back end and used supervised machine learning to create the model. We also used MongoDB to store the data.
- Python
- Flask
- Bootstrap
- HTML
- CSS
- MongoDB
- Scikit Lean
- Supervised ML
