I am a data scientist currently looking for work. I have a background in Supply Chain and a passion for learning.

This project aims to create a model that will capture any observations on the field including game situations and offense, that will help to make a better decision on defensive side of the ball. The model looks at the positioning of the players on offense on the field and the different types personnel on the field (WR/TE/RB). Teams employ different offensive personnel breakdowns (WR/TE/RB). These positions will be explained in more detail later. The model also looks at the game situation of the down situation and the yards needed for a first down. The goal of this model is to address the issue of making decisions analytically and quickly by calculating the EPA of the next play and recommending the coverage that predicts the lowest EPA. EPA is short for Expected Points Added, which will be the target variable for our model.

I created a model that used the King County data set that predicted the housing prices in that region. In specific, I built a linear regression model and looked to isolate the top variables that affect price while also generating additional features to better predict prices. Polynominal models were also generated in order to generate a model of better fit. The model has its shortcomings as there can be many factors that are missing from the dataset.

My partner and I built a various Multi-Classification models (Logistic Regression, KNN, Decision Trees, Random Forest, and XGBoost) to prove that music can be classified by the time period they originate from by their musical attributes. New artist discovery through genre classification not only benefits users, but also artists and Spotify. Unknown artists benefit from more methods of discovery and Spotify potentially gains more revenue and more data.