### What is Normalization?

Normalization, also known as ‘Unit-Length Scaler’, is a ‘Feature Scaler’ that can be used when preprocessing numerical data.

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Normalization, also known as ‘Unit-Length Scaler’, is a ‘Feature Scaler’ that can be used when preprocessing numerical data.

Feature Standardization is a technique for pre-processing numerical raw data during the creation of your training data.

Two basic pre-processing techniques, applicable to Numerical Features, are ‘Centering’ and ‘Scaling’.

Categorical features are features that can take a limited, and usually a fixed number of possible values.

Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.

If outliers are detected and it is deemed appropriate to exclude them, they can be removed from the data before performing standardization.

Discretization refers to the process of binning a continuous variable into a discrete number of buckets.

Different categories of missing data are: (a) Missing Completely at Random (MCAR), (b) Missing at Random (MAR), and (c) Missing Not at Random

Mean Imputation, Mode Imputation, Extreme Value Imputation, Nearest Neighbor Imputation, Expectation Maximization

This refers to a special case of discretization in which a continuous variable is transformed into a categorical representation that only consists of two bins.

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