My name is Samuel DiMaggio. I have an M.S. in Data Science from Maryville University in St. Louis, MO and a B.S. in Physics from Westminster College in Salt Lake City, UT.
When I was a kid, I was interested in becoming an astrophysicist. I remember reading so many astronomy related books with illustrations of black holes and massive red stars. In high school, I gravitated towards reading as many books from Physicists and Mathematicians such as Michio Kaku, Stephen Hawking, Paul Dirac, Richard Feynman, Lawrence Krauss, Roger Penrose, and others. After graduating high school, I enlisted in the USAF and served for six years. For the first four years, I was an avionics technician specializing in Electronic Warfare and Computer Systems. For the remaining two years, I was assigned special duty as a Maintenance Operations Center Controller, controlling operations pertaining to maintenance of multiple aircrafts.
Once my enlistment ended, I moved to Salt Lake City, Utah to pursue a B.S. in Physics. My passion for physics carried me through my education at Westminster College. Here I found a new interest in coding after being exposed to programming languages such as Python, R, SAS, MATLAB, and Mathematica. After graduating, I decided to pursue a graduate degree in data science from Maryville University in St. Louis, MO. Before and during my degree, I would research and study topics of machine learning and deep learning. Recently, I graduated from Maryville University, and I am continuing to research and expand my knowledge in data science related topics. Below in the portfolio section, you can find some of the projects I have been working on since graduation.
Below, you can find some of the projects I have been working on since graduation.
This tool utilizes tokenization from the NLP library to search through a given Safety Data Sheet to identify chemicals of interest that pertain to Federal and International regulatory requirements. Containing a simple GUI input and output for a easy user experience. This link demonstrates how to convert this python file to a executable file using Nuitka.
The purpose for this project is to use machine learning techniques, such as logistical regression, support vector machine, and random forest algorithms to predict multiple classes for a set of data. For this project, in particular, will focus on a dataset set of 240 stars to predict classes of star based on features provided by the dataset and evaluate which technique works best for these predictions.
Here I created a simple book recommender system using tensorflow to identify possible books a user may enjoy based off a rating system from 1 to 10. I had the program to randomly select a user and recommend a list of 10 books that the user might enjoy.
Comparing and optimizing linear regression models on an aerodynamic and acoustic .dat file.
Working with a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. I will be performing text classification while building a Recurrent Neural Network (RNN)