
Customer Segmentation
Customer segmenation project with an actual dataset from a UK-based online store. Used PCA, KMeans and Plotly's interactive data viz library.
Perpetually curious.
Hi. My name is Sam Ouimet. I have had a life long interest in business and technology, and over the past few years, this has bubbled over into becoming a professional Analyst and an aspiring Data Scientist. Since leveraging sports analytics to compete in thousands of daily fantasy sports competitions while in college, my mind has been geared towards uncovering insights with data. Post-college, I have worked as a Markets Analyst and Editor for the world's leading blockchain media company, CoinDesk, and have advanced my analytical skillset by completing the only accredited data science bootcamp in the United States, Metis. Along the way, I have worked on a number of data analysis and machine learning projects end-to-end, most of which can be viewed in this digital portfolio.
Please enjoy and feel free to reach out if you would like to get in touch.
Below are some of my favorite data science projects that I completed while studying at Metis or in my free time.
Customer segmenation project with an actual dataset from a UK-based online store. Used PCA, KMeans and Plotly's interactive data viz library.
Using a Telco customer and product dataset from Kaggle, I used several classification algorithms to predict customer churn.
Created a KPI dashboard in Tableau to display the sales and profit information from the famous Superstore dataset by year, product, location, and more.
NLP and topic modeling performed on full-length movie scripts to identify "how much" of various genres or themes are present in a given movie. Includes a simple recommendation system based on the cosine similarity of the movie-topic vectors.
Used the Johns Hopkins COVID-19 dataset and Tableau to create a dashboard that displays the growth of confirmed cases and deaths around the world.
I web scraped several years of NFL game, vegas, and team power ranking data to create a unique dataset for predicting individual game outcomes with various classification algorithms.
Used a company's sales data to answer critical business questions such as, the best time to display an ad, which products are most often sold together, and more. Analysis completed using Python.
Web scraped NFL team based statistics and predicted the next year win total for a given team using linear regression.
If you would like to contact me, please see my various social media profiles or email address located on my resume and the side menu of this webpage. You can also send me an email directly with the form below: