Churn Forecasting

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prerequisites
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.

project author

Carl Gold
Carl Gold is the senior data science manager for financial startup Migo.money. 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.

prerequisites

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:

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

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