Transform the 2 numerical features (age and education_years) into 2 (nominal) categorical features.

Transform the 2 numerical features (age and education_years) into 2 (nominal) categorical features. Specifically, use equal-width binning with the following 3 bins for each numerical feature: low, mid, and high. Once you do that, all the 5 descriptive features in your dataset will be categorical. Your dataset’s name after Task 1 needs to be df_all_cat. Please make sure to run the following code for marking purposes:
# so that we can see all the columns
pd.set_option(‘display.max_columns’, None)
print(df_all_cat.shape)
df_all_cat.head()
###
# please run below in a separate cell!!!
for col in df_all_cat.columns.tolist():
print(col + ‘:’)
print(df_all_cat[col].value_counts())
print(‘********’)
HINT: You can use the cut() function in Pandas for equal-width binning.

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