课程

Marketing Analytics: Predicting Customer Churn in Python

中级技能水平

更新时间 2023年12月

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

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PythonExploratory Data Analysis4 小时13 视频45 练习3,550 经验值18,222成就声明

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课程描述

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.

先决条件

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

50 经验值

Defining customer churn

50 经验值

Exploring customer churn

50 经验值

Grouping and summarizing data

50 经验值

Summary statistics for both classes

100 经验值

Churn by State

100 经验值

Exploring your data using visualizations

50 经验值

Exploring feature distributions

100 经验值

Customer service calls and churn

100 经验值

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

50 经验值

Identifying features to convert

50 经验值

Encoding binary features

100 经验值

One hot encoding

100 经验值

Feature scaling

100 经验值

Feature selection and engineering

50 经验值

Dropping unnecessary features

100 经验值

Engineering a new column

100 经验值

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

50 经验值

Predicting whether a new customer will churn

100 经验值

Training another scikit-learn model

100 经验值

Evaluating Model Performance

50 经验值

Creating training and test sets

100 经验值

Check each sets length

50 经验值

Computing accuracy

100 经验值

Model Metrics

50 经验值

Confusion matrix

100 经验值

Varying training set size

100 经验值

Computing precision and recall

100 经验值

Other model metrics

50 经验值

100 经验值

Area under the curve

100 经验值

Precision-recall curve

50 经验值

100 经验值

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

50 经验值

Tuning the number of features

100 经验值

Tuning other hyperparameters

100 经验值

Randomized search

100 经验值

Feature importances

50 经验值

Visualizing feature importances

100 经验值

Improving the plot

100 经验值

Interpreting feature importances

50 经验值

Adding new features

50 经验值

Does model performance improve?

100 经验值

Computing other metrics

100 经验值

Final thoughts

50 经验值

Marketing Analytics: Predicting Customer Churn in Python

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