Preprocessing Categorical Data: The Why and How of One-Hot Encoding

One-Hot Encoding: Why and How?

For many ML models, categorical data must be converted into numerical form. One-hot encoding is a popular method, as it turns each category into its binary column (1 if the category is present, 0 if not).

Why One-Hot Encoding?

Most machine learning models, like linear regression, expect numerical inputs. One-hot encoding allows categorical features to be used effectively in these models, allowing them to understand non-numeric data.

❓In the code below, we have the input and output tables. Even though there are three categories in the first table, why is it enough to create two columns instead of three? Comment your answer👇!

#DataScience #MachineLearning #Regression #Python #OneHotEncoding