Highlights
1. With a view to uncovering customer churn patterns, investigate how each relevant variable is associated with Churn.
a. For each relevant categorical variable, construct a distribution of the variable with a churn overlay. You may wish to normalize to increase contrast.
b. For each relevant numerical variable, construct a histogram of the variable with a churn overlay. You may wish to normalize to increase contrast.
c. For the subset of records in 5a, compare the proportion of churners in this subset with the proportion of churners not in the subset (Note: Don't compare against the entire data set, which includes your subset).
2. Find a pair of numeric variables which are interesting with respect to churn. That is, for a pair of variables, construct a scatter plot with a churn overlay. If things look uniform, then this is not particularly interesting. We are looking for differences within the scatter plot (churn vs. non-churn), which can help us understand the relationship between the two variables with churn. Now, if there seems to be a horizontal or vertical differentiation, then this is not bivariately interesting, as the churn behavior is altering only along one of the axes. We want to find churn behavior changing simultaneously along both axe
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