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Course

Marketing Analytics: Predicting Customer Churn in Python

IntermediateSkill Level

4.8+

Updated 12/2023

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

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PythonExploratory Data Analysis4 hr13 videos45 Exercises3,550 XP18,222Statement of Accomplishment

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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 with 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.

Prerequisites

Data Manipulation with pandas

1

Exploratory Data Analysis

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

Welcome to the course

Defining customer churn

Exploring customer churn

Grouping and summarizing data

Summary statistics for both classes

Churn by State

Exploring your data using visualizations

Exploring feature distributions

Customer service calls and churn

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.

Data preprocessing

Identifying features to convert

Encoding binary features

One hot encoding

Feature scaling

Feature selection and engineering

Dropping unnecessary features

Engineering a new column

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.

Making Predictions

Predicting whether a new customer will churn

Training another scikit-learn model

Evaluating Model Performance

Creating training and test sets

Check each sets length

Computing accuracy

Model Metrics

Confusion matrix

Varying training set size

Computing precision and recall

Other model metrics

Area under the curve

Precision-recall curve

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.

Tuning your model

Tuning the number of features

Tuning other hyperparameters

Randomized search

Feature importances

Visualizing feature importances

Improving the plot

Interpreting feature importances

Adding new features

Does model performance improve?

Computing other metrics

Final thoughts

Marketing Analytics: Predicting Customer Churn in Python

Course
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FAQs

What dataset does this course use for churn prediction?

You work with the Telco Churn Dataset, exploring it with pandas and Seaborn before building machine learning models with scikit-learn.

What steps does the course cover from data exploration to prediction?

You move through exploratory data analysis, feature selection and engineering, building supervised learning models, and finally tuning hyperparameters to improve model performance.

Do I need machine learning experience to take this course?

Basic familiarity with pandas and Python is required, but no prior machine learning experience is needed. The course introduces scikit-learn model building from the ground up.

What industries use the churn prediction skills taught here?

Churn prediction is used across telecommunications, cable TV, SaaS, subscription services, and any business where retaining customers is critical to revenue.

Does the course explain how to interpret what drives churn?

Yes. The final chapter on model tuning helps you gain a better understanding of the key drivers of customer churn, which you can then communicate back to the business.

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