Ordinal encoding is similar to label encoding, except there's an order to the encoding. The output encoding will start from 0 and end at one less than the size of the categories. Let's look at an example containing employee grades such as G0, G1, G2, G3, and G4. These five grades have been encoded with ordinal integer values; that is, G0 is 0, G1 is 1, G2 is 2, G3 is 3, and G4 is 4. We can define the order of the values as a list and pass it to the category parameter. The ordinal encoder uses the integer or numeric values to encode. Here, the integer and numeric values are ordinal in nature. This encoding helps machine learning algorithms take advantage of this ordinal relationship.
Let's take a look at the following OrdinalEncoder example:
# Import pandas and OrdinalEncoder
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
# Load the data
data=pd.read_csv('employee.csv')
# Initialize OrdinalEncoder with order
order_encoder=OrdinalEncoder...
