Fighting Churn

Churn Forecasting you own this product

This project is part of the liveProject series Fighting Churn with Manning's liveBook Data
basic Python • basic PostgreSQL • basic machine learning
skills learned
XGBoost machine learning model churn forecasting • logistic regression model churn forecasting • explain machine learning model results
Carl Gold
1 week · 8-12 hours per week · INTERMEDIATE

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Look inside
In this liveProject, you’ll develop a simple machine learning algorithm that can determine from data metrics which of your customers are likely to churn out of an online business. You’ll develop an XGBoost machine learning model and explain its predictions, then build a logistic regression statistical forecasting and analysis model to also predict churn. By the time you’re done, you’ll have two reliable and automated tools for spotting when customers are likely to leave so that your marketing team can easily intercede.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Carl Gold
Carl Gold is the senior data science manager for financial startup He has previously worked as chief data scientist for Zuora, the industry-leading subscription management platform. He has a PhD from the California Institute of Technology.


This liveProject is suitable for anyone with a basic background in programming and data analysis, who wants to really make a difference to a business's churn. To begin this liveProject you will need to be familiar with:

  • Basic Python
  • Basic PostgreSQL
  • Basic machine learning cross validation and forecasting
  • Basic logistic regression model fitting and forecasting

you will learn

In this liveProject, you’ll master practical techniques from wrangling a raw set of data into something usable by your company in fighting churn.

  • Fit the parameters of an XGBoost machine learning model with cross validation
  • Use the XGBoost machine learning model to forecast on new customers
  • Explain the XGBoost model using SHAP analysis
  • Prepare the data and fit a Logistic Regression model
  • Use the logistic regression model to forecast on new customers
  • Explain the logistic regression model with the coefficients


You choose the schedule and decide how much time to invest as you build your project.
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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