Categorical Variables for Machine Learning Algorithms

One-hot encoding in Python and on the data warehouse

While most machine learning algorithms only work with numeric values, many important real-world features are not numeric but rather categorical. As categorical features, they take on levels or values. These can be represented as various categories such as age, state, or customer type for example. Alternatively, these can be created by binning underlying numeric features, such as identifying individuals by age ranges (e.g., 0–10, 11–18, 19–30, …

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