Kris Ferris

About

Greetings! My name is Kris Ferris and I have a passion for data analytics. Leveraging my vast logistics analysis background, I am keen on applying this expertise to my next chapter. I'm committed to harnessing data analytics to steer strategic business choices and enhance performance. I've recently earned my Master's in Data Science and a Graduate Certificate in Business Analytics. Alongside my extensive experience with SAP, Lean Six Sigma Green Belt certification and Demonstrated Senior Logistician status, these qualifications highlight my comprehensive education and experience, positioning me perfectly for a future in analytics. On a personal note, I value time spent outdoors with my family, golfing, and coaching youth basketball. I welcome the chance to learn from your insights in the field and discuss how I might aid in the success of your team.

Projects

Machine Learning Classification Project

This project utilizes machine learning classification method to identify an apple, banana, or an orange, specifically through the use of Google Teachable Machine and the Keras Model. For more details, please check out my GitHub repository.

Google Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. It allows you to train models directly in your browser by teaching a machine using your camera, live in the browser – no coding required.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation and making deep learning models easy to build and run.

NFL Fantasy Football Points SQL Project

This project leverages SQL to analyze and predict NFL player performance in terms of fantasy points. Data extraction, transformation, and loading (ETL) processes were performed, as well as complex SQL queries to provide insights and make forecasts for fantasy football players.

For more details, please check out the project on GitHub.

Rotten Tomatoes Movie Analysis

This project conducts a comprehensive analysis of Rotten Tomatoes movie data using Python with the Polars package. The data set, current as of October 2020, provided a broad foundation for insights into movie trends, ratings, and audience preferences.

Python and the Polars package were utilized to manipulate and analyze the dataset, highlighting the flexibility and efficiency of these tools for data science projects.

For more details, please check out the project on GitHub.

Contact

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';