Used the “Adult” Data Set from the UCI Machine Learning Repository with the goal of comparing the classification methods of Neural Networks and applying PCA to logistic regression. The goal was to successfully classify whether a person earns a salary of $50,000 USD or not based on certain features about the individual.

Natrual Language processing: text classification using on a series of texts in the Brown Corpus. Utilized two methods of NLP analysis: Bag of Words and TF-IDF. Measured accuracy across three different models: Random Forest Classifier, Logistic Regression, and Multinomial Naive Bayes. TF-IDF resulted in higher accuracy overall, achieving a high of 82% with Logisitc Regression.

Demonstration of base data science fundamentals. Generated visuals and reached insights based on United Nations data on gender in the Middle East. Sought to explore gender disparity across multiple aspects of life (education, work, etc.) and their relationship with one another.

Project in progress. Full stack application to streamline specific accounting work. Utilizes a iOS front-end written in Swift, a RESTful API created with Django, and another front-end with React. Employees are to fill out a form on an iOS device which will then communicate to the API endpoints and store the data in a PostgreSQL database. The employer will then have access to the data on the React front-end and will be able to make complex queries to better understand transactions in their entirety.