How to Leverage Data Science to Predict Customer Outcomes
Learn how to employ machine learning to predict customer outcomes and focus on the niche areas of your organization that need attention.
The use of data science is transformative to organizations, leading to data-driven decision-making, improved business performance, and reduced risks. While data science may seem daunting at first, it is an integral part of capitalizing on your organization’s data insights to make critical predictions through machine learning. In this webinar, Tessellation consultant, Joe McFarren, will demonstrate how to leverage machine learning to determine how to predict customer outcomes based on a variety of factors.
Joe McFarren will take a deep dive into how to leverage the K-Nearest Neighbors (KNN) algorithm in Python to classify your data. Understanding how to segment your existing data into actionable areas leveraging KNN can allow you to focus on niche areas of your business that need attention.
In this webinar, you will learn:
- The basics of classification
- How to use the KNN algorithm in Python
- How to interpret the results for business outcomes
Watch the Webinar:
About the Speakers:
Joe McFarren’s first introduction to data visualization was as an academic research scientist at the University of Minnesota Masonic Cancer center performing experiments to observe and track brain tumor growth. This led him to a path into consulting, where he has had experience in healthcare, medical devices, consumer goods, media, 3D printing, manufacturing, and a few places in between.
Joe has been a consultant for 4 years undertaking various roles as a business analyst, project manager, scrum master, data analyst, and developer, and he has a strong interest in data science and how to apply data best practices to our clients.
As a consultant at Tessellation, Joe interacts with our clients to understand their data needs and problems, helps craft a solution, and empowers the client to own the end product so there are no gaps in the hand-off process.
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