This document discusses methods for handling categorical data in machine learning models. It describes categorical variables as data that falls into fixed categories like phone brands. It recommends either dropping unuseful categorical variables, label encoding which assigns integer values to categories, or one-hot encoding which separates categories into binary columns. Label encoding works well for decision trees while one-hot encoding preserves differences between categories.

1. How to handle Categorical Data
By
Srinivas Rao PrithviNag Kolla,
Masters in Data Science,
University of North Texas,
Email: prithvikolla8@gmail.com

2. Categorical Variable:
Generally Data falling into a fixed set of categories is called a categorical data.
Ex:
Survey of what type of phone brand people own comes under categorical data.
Id Name Phone Brand
1 Alex Apple
2 George Nokia
3 Chen Apple
4 prithvi Samsung
Dropping Categorical Variables:
If the columns in the data set have categorical values , which are not useful for
modeling , we can drop them.

3. Label Encoding:
Giving a unique integer value for the labels in the categorical column.
Ex:
Phone Brand
Apple
Nokia
Jio
Apple
Phone Brand
0
1
2
0
Label Encoding
Decision Trees and Random Forests work well with Label Encoding.
‘Apple’(0) < ‘Nokia’(1) < ‘Jio’(2)

4. One – Hot – Encoding
• As previously in Label Encoding, we gave an order based unique values to the
labels.
• It doesn’t work well, so we use the method of separating the categorical values
into columns and give ‘1’ as they are present in that row, if not ‘0’.
Ex:
Phone Brand
Apple
Nokia
Apple
Jio
Nokia
Apple Nokia Jio
1 0 0
0 1 0
1 0 0
0 0 1
0 1 0
One-Hot-Encoding