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Marketing Analytics: Predicting Customer Churn in Python

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

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4 Hours13 Videos45 Exercises12,409 Learners3550 XPMarketing Analytics Track

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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.

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    Welcome to the course
    50 xp
    Defining customer churn
    50 xp
    Exploring customer churn
    50 xp
    Grouping and summarizing data
    50 xp
    Summary statistics for both classes
    100 xp
    Churn by State
    100 xp
    Exploring your data using visualizations
    50 xp
    Exploring feature distributions
    100 xp
    Customer service calls and churn
    100 xp
  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.

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In the following tracks

Marketing Analytics


loreLore DirickyashasYashas 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.
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