One of the most crucial preprocessing steps in any machine learning project is feature encoding. It is the process of turning categorical data in a dataset into numerical data. It is essential that we perform feature encoding because most machine learning models can only interpret numerical data and not data in text form.

In this article, we will learn:

• The difference between a nominal variable and an ordinal variable
• How OneHotEncoder and OrdinalEncoder can be used to encode these variables respectively
• Why the Scikit-learn library is preferred over the Pandas library when it comes to encoding categorical features

As usual, I will demonstrate these concepts through a practical case study using the students’ performance in exams dataset on Kaggle.

You can find the complete notebook on my GitHub here.

#machine-learning #scikit-learn #feature-encoding #data-preprocessing #data-science