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  3. Marketing Analytics: Predicting Customer Churn in Python
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Exercises

Customer service calls and churn

You've already seen that there's not much of a difference in account lengths between churners and non-churners, but that there is a difference in the number of customer service calls left by churners.

Let's now visualize this difference using a box plot and incorporate other features of interest - do customers who have international plans make more customer service calls? Or do they tend to churn more? How about voicemail plans? Let's find out!

Recall the syntax for creating a box plot using seaborn:

sns.boxplot(x = "X-axis variable",
            y = "Y-axis variable",
            data = DataFrame)

If you want to remove outliers, you can specify the additional parameter sym="", and you can add a third variable using hue.

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  • Create a box plot with 'Churn' on the x-axis and 'CustServ_Calls' on the y-axis.