Mark Peterson
Mark Peterson

Senior Data Scientist at Alliance Data

Mark is a senior data scientist who holds degrees in Predictive Analytics, Agriculture Economics, and Animal Science. He has worked on a variety of big data and machine learning projects across the US and Latin America including customer churn, part failures, smart cities, and NLP. He's interested in using AI to improve business processes and lives.

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Collaborator(s)
  • Lore Dirick

    Lore Dirick

  • Benjamin  Feder

    Benjamin Feder

Course Description

Understanding customer behavior and when a customer will cancel service, or churn, is a problem that is common across a variety of idustries. From Telecommunications, Cable TV, SaaS, and Streaming Services, customers are deciding whether to cancel or renew their service based on a variety of factors. As businesses more actively manage their bottom line, data scientists are tasked with finding data driven models that will help retain the most profitable customers. This course will provide you a valuable roadmap to create your own customer churn model. An entire churn analytics workflow that starts with exploratory data analysis of a telecommuncation churn dataset, and ends with providing the business actionable insights that include a churn model with probability of churn, the most important features that lead to a churn event, and buy-in from the business to ensure the model is deployed and successful. Along the way you will also learn to scale and transform data, create new features from the existing data, and train a churn model using a supervised machine learning technique in Python and scikit-learn.

  1. Exploratory Data Analysis for Customer Churn

  2. Data Preparation and Feature Engineering for Customer Churn Model

  3. Fitting a Customer Churn Model

  4. Validating Customer Churn Model and Making Recommendations Back to the Business

Course Instructor

Mark Peterson
Mark Peterson

Senior Data Scientist at Alliance Data

Mark is a senior data scientist who holds degrees in Predictive Analytics, Agriculture Economics, and Animal Science. He has worked on a variety of big data and machine learning projects across the US and Latin America including customer churn, part failures, smart cities, and NLP. He's interested in using AI to improve business processes and lives.

See More
Collaborator(s)
  • Lore Dirick

    Lore Dirick

  • Benjamin  Feder

    Benjamin Feder

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