Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:

- Methods work with different data dimensions;
- The order of the categories is not important for label encoding.


from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Simple categorical variable
fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana'])

# Create label encoder object
le = LabelEncoder()

# Fit and transform the categorical variable using label encoding
fruits_encoded = le.fit_transform(fruits)

# Print the encoded values
print(fruits_encoded)

Creating a machine learning model seems to be your most challenging and essential task. But first, we have to work with data! Learn how to process datasets and fully prepare them for use. Numerical, categorical, and temporal data await you in our course.

Different types of data? How to work with them? If your eyes are wide open, don't worry, let's start with a brief overview of the pandas library and learn how to work with it in the future.

This chapter discusses in detail how to work with quantitative data, what methods it is processed with, how data scaling and normalization differ, and much more.

Is categorical data as simple as you think it is? Find out what is the complexity of processing and working with it.


Time series data processing is the process of handling, analyzing, and preparing data that is presented as a sequence of temporally ordered values. Find out what steps it includes in this section.

Did you know that you can extract even more values from your data and create more informative features? In this section, you will learn how to work with feature engineering.

You have reached the end of this course. Let's test your knowledge! There are 3 tasks for you to solve.

Label Encoding of the Target Variable

Tarea

Data Preprocessing

Label Encoding of the Target Variable

Tarea