Guide to Encoding Categorical Features Using Scikit-Learn For Machine Learning

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. 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 up on my GitHub here.