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Analyzing Cardiac Medical Devices with a Machine Learning Method
My PhD dissertation topic involved analyzing adverse cardiac medical device events from the FDA’s MAUDE database. Focusing on years 1997 through 2017, textual data was analyzed with machine learning methods to examine the effects of specific failures on patients. The purpose of this project was to create awareness regarding cardiac medical devices-creating a standard in research, to promote information sharing and knowledge transfer, as well as informing the general public.

Crime Analysis
Supervised Learning: Police Incidents within various census tracts in Albuquerque, New Mexico were predicted based upon factors such as median age and income using supervised learning and statistical approaches. This analysis can be beneficial for law enforcement to gauge where to potentially expect calls to originate from.

Customer Segmentation
Unsupervised Learning: This project analyzed customer spending using unsupervised learning techniques for further insight about customer base. This analysis would be useful in understanding the needs of customers and applying resources to meet their needs in a more prompt manner.

Data Wrangling
A mini-project to clean and normalize U.S. Geological Survey (USGS) data and merged with a data obtained from The Indian Health Service. The purpose of this project is to demonstrate data wrangling (data pre-processing) that is typical before analysis.

Finding Donors
Supervised Learning: A model was designed to predict whether an individual earns more than $50,000 annually to identify potential donors for an organization. This type of analysis would be useful for non-profits or organizations that rely on donations as a source of revenue.

Influenza Deaths in New Mexico, 2012-2017
Tableau Visualization: This visualization conveyed the death rates due to influenza in the state of New Mexico from 2012 to 2017, by county. Bernalillo and Dona Ana counties had the highest percentage of death during this time.

Poverty, 2017
This project provided a visualization of poverty in The United States by state and county for year 2017. The purpose of this is to demonstrate how to communicate results in a visual format to be easily understood by a wider range of audiences.