6. Analysis of Credit Card Defaulters

Activity 6.01: Evaluating the Correlation between Columns Using a Heatmap

1. Plot the heatmap for all the columns in the DataFrame (other than the ID column) by using sns.heatmap and keep the figure size as 30,10 for better visibility:

   sns.set(rc={'figure.figsize':(30,10)})
   sns.set_context("talk", font_scale=0.7)

2. Use Spearman as the method parameter to compute Spearman's rank correlation coefficient:

   sns.heatmap(df.iloc[:,1:].corr(method='spearman'), \
               cmap='rainbow_r', annot=True)

   The output of the heatmap is as follows:

   Figure 6.28: Heatmap for Spearman's rank correlation

3. In order to get the exact correlation coefficients of each column with the DEFAULT column, apply the .corr() function on each column with respect to the DEFAULT column:

   df.drop("DEFAULT", axis=1)\
   .apply(lambda x: x.corr(df.DEFAULT...