Interactive Course

Marketing Analytics: Predicting Customer Churn in Python

Learn how to use Python to analyze customer churn and build a model to predict it.

  • 4 hours
  • 13 Videos
  • 45 Exercises
  • 8,663 Participants
  • 3,550 XP

Loved by learners at thousands of top companies:


Course Description

Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries, from telecommunications to cable TV to SaaS, and a company that can predict churn can take proactive action to retain valuable customers and get ahead of the competition. This course will provide you a roadmap to create your own customer churn models. You’ll learn how to explore and visualize your data, prepare it for modeling, make predictions using machine learning, and communicate important, actionable insights to stakeholders. By the end of the course, you’ll become comfortable using the pandas library for data analysis and the scikit-learn library for machine learning.

  1. 1

    Exploratory Data Analysis


    Begin exploring the Telco Churn Dataset using pandas to compute summary statistics and Seaborn to create attractive visualizations.

  2. Preprocessing for Churn Modeling

    Having explored your data, it's now time to preprocess it and get it ready for machine learning. Learn the why, what, and how of preprocessing, including feature selection and feature engineering.

  3. Churn Prediction

    With your data preprocessed and ready for machine learning, it's time to predict churn! Learn how to build supervised learning machine models in Python using scikit-learn.

  4. Model Tuning

    Learn how to improve the performance of your models using hyperparameter tuning and gain a better understanding of the drivers of customer churn that you can take back to the business.

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Lloyd's Banking Group


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Harvard Business School


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Decision Science Analytics @ USAA

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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  • Lore Dirick

    Lore Dirick

  • Yashas Roy

    Yashas Roy

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