Label Encoder in ML
In this exercise, we will learn about the Label Encoder in ML.
Label Encoder is a preprocessing tool in Scikit-Learn (a Python library) that is used to convert categorical labels into numerical values. It is particularly useful for machine learning models, as many of them require input features to be numeric.
Why Label Encoding? Many machine learning algorithms do not work with text or string data. Therefore, categorical variables (like "red," "blue," "green") need to be converted into numerical values.
How Label Encoding Works Label Encoding assigns each unique value in a categorical column an integer value, starting from 0.
Example: Import the required module and load the data from the CSV file.
Python
# Import the pandas package import pandas as pd import numpy as np # Load Data into Pandas DataFrame df = pd.read_csv("employees.csv") df
The output of the above code is shown below:

Python
# Import the LabelEncoder module from sklearn.preprocessing import LabelEncoder # Initialize the object label_encoder = LabelEncoder() # Create or replace an existing column with the same name df['Gender'] = label_encoder.fit_transform(df['Gender']) df
The output of the above code is shown below:

Python
# Create or replace an existing column with the same name df['Company'] = label_encoder.fit_transform(df['Company']) df
The output of the above code is shown below:
