figure - histogram depicting developer income distribution after box-cox transform ( loptimalthe distribution of the transformed numeric values for developer income after the box-cox distribution also look similar to the one we had obtained after the log transform such that it is more normal-like and the extreme right skew that was present in the raw data has been minimized here feature engineering on categorical data so farwe have been working on continuous numeric data and you have also seen various techniques for engineering features from the same we will now look at another structured data typewhich is categorical data any attribute or feature that is categorical in nature represents discrete values that belong to specific finite set of categories or classes category or class labels can be text or numeric in nature usually there are two types of categorical variables--nominal and ordinal nominal categorical features are such that there is no concept of ordering among the valuesi it does not make sense to sort or order them movie or video game genresweather seasonsand country names are some examples of nominal attributes ordinal categorical variables can be ordered and sorted on the basis of their values and hence these values have specific significance such that their order makes sense examples of ordinal attributes include clothing sizeeducation leveland so on in this sectionwe look at various strategies and techniques for transforming and encoding categorical features and attributes the code used for this section is available in the code files for this you can load feature_engineering_categorical py directly and start running the examples or use the jupyter notebookfeature engineering on categorical data ipynbfor more interactive experience before we beginlet' load the following dependencies in [ ]import pandas as pd import numpy as np