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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 Exercises
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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

    Free

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

    Play Chapter Now
    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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    Data preprocessing
    50 xp
    Identifying features to convert
    50 xp
    Encoding binary features
    100 xp
    One hot encoding
    100 xp
    Feature scaling
    100 xp
    Feature selection and engineering
    50 xp
    Dropping unnecessary features
    100 xp
    Engineering a new column
    100 xp
  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.

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    Making Predictions
    50 xp
    Predicting whether a new customer will churn
    100 xp
    Training another scikit-learn model
    100 xp
    Evaluating Model Performance
    50 xp
    Creating training and test sets
    100 xp
    Check each sets length
    50 xp
    Computing accuracy
    100 xp
    Model Metrics
    50 xp
    Confusion matrix
    100 xp
    Varying training set size
    100 xp
    Computing precision and recall
    100 xp
    Other model metrics
    50 xp
    ROC curve
    100 xp
    Area under the curve
    100 xp
    Precision-recall curve
    50 xp
    F1 score
    100 xp
  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.

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    Tuning your model
    50 xp
    Tuning the number of features
    100 xp
    Tuning other hyperparameters
    100 xp
    Randomized search
    100 xp
    Feature importances
    50 xp
    Visualizing feature importances
    100 xp
    Improving the plot
    100 xp
    Interpreting feature importances
    50 xp
    Adding new features
    50 xp
    Does model performance improve?
    100 xp
    Computing other metrics
    100 xp
    Final thoughts
    50 xp

In the following tracks

Marketing Analytics with Python

Datasets

Telco Churn Dataset

Collaborators

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Yashas Roy

Prerequisites

Data Manipulation with pandas
Mark Peterson HeadshotMark 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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