Marketing Analytics: Predicting Customer Churn in Python

Learn how to use Python to analyze customer churn and build a model to predict it.
Start Course for Free
4 Hours13 Videos45 Exercises10,630 Learners
3550 XP

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of 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.
    Play Chapter Now
  2. 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.
    Play Chapter Now
  3. 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.
    Play Chapter Now
  4. 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.
    Play Chapter Now
In the following tracks
Marketing Analytics
Lore DirickYashas Roy
Mark Peterson Headshot

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

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA