All of my data science projects are written in Python, which is quickly becoming the industry standard in data science. Python allows for fine tuned control over machine learning algorithms and data preparation, execution, and visualization.
I am proficient in the use of SQL for storing, manipulating and retrieving data in relational database systems. Structured Query Language is vital to pulling necessary data from databases for data science work.
Highly proficient in all areas of development of machine learning pipelines and modeling. Performance testing, data visualization, exploratory data analysis, data cleaning, model preparation, ML model development, and experimental design are all areas where I excel.
This project explored and compared the performance of new and legacy machine learning models with a history of use in the financial industry for bankruptcy detection. Bankruptcy is a legal process through which people or Read more…
The busiest passenger rail line in the United States, Amtrak also operates passenger rail service; together the NJ Transit and Amtrak operate nearly 750 trains across the NJ Transit rail network. This data is organized Read more…
Customer churn, also known as customer attrition or customer turnover, is the loss of a client or customer. Customer churn is a key business metric for many different industries; in this case telecommunications technologies. Predicting Read more…
Heart disease remains the leading cause of death worldwide — accounting for one-third of deaths in 2019. This makes heart disease a significant problem for all of us, and one that we should wish to Read more…
This dataset is from the NYC MTA buses data stream service. It shows in 10 minute increments the buses location, route, bus stop and bus schedule. We can cluster this data for a variety of Read more…