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.
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