After data cleaning, it's time to transform your data into a form that models can learn from effectively. Feature engineering is so critical that it's often said to account for 80% of model performance—a testament to its significance in the ML pipeline.
Today, we'll cover three core transformations: scaling to align numeric feature ranges, encoding to convert categorical data to numbers, and binning to group continuous values into discrete intervals. We'll explore why each technique is necessary for different algorithms.
Many algorithms are sensitive to the scale (magnitude) of features