Categorical Encoding

Categorical encoding is a method used in data analysis and machine learning to convert non-numeric categories—like colors, brands, or city names—into numbers so algorithms can process them effectively. Since computers work best with numbers, encoding transforms categories into numerical values or structured formats, preserving their meaning. Common techniques include assigning unique numbers to each category (label encoding) or creating binary columns for each category (one-hot encoding). This process helps models better understand patterns involving categorical data, enabling more accurate predictions and insights.