Normalization and standardization

At different points during our journey in this book, we've already talked about and used normalization and standardization. For instance, before applying K-Nearest Neighbors (KNN) in Chapter 7, Classification, and before using K-means on our dataset in Chapter 8, Clustering Analysis, we used normalization. Furthermore, before applying Principal Component Analysis (PCA) to our dataset for unsupervised dimension reduction in Chapter 13, Data Reduction, we used standardization.

Here is the general rule of when we need normalization or standardization. We need normalization when we need the range of all the attributes in a dataset to be equal. This will be needed especially for algorithmic data analytics that uses the distance between the data objects. Examples of such algorithms are K-means and KNN. On the other hand, we need standardization when we need the variance and/or the standard deviation of all the attributes to be equal. We saw an example...