Preprocessing Data for Machine Learning Models

Preprocessing data before applying any machine learning model can improve the accuracy of the model to a large extent. Therefore, it is important to preprocess data before applying a machine learning algorithm. Preprocessing data consists of the following methods: Standardization, Scaling, and Normalization

Standardization

Most machine learning algorithms assume that all features are centered at zero and have variance in the same order. In the case of linear models such as logistic and linear regression, some of the parameters used in the objective function assume that all the features are centered around zero and have unit variance. If the values of a feature are much higher than some of the other features, then that feature might dominate the objective function and the estimator may not be able to learn from other features. In such cases, standardization can be used to rescale features such that they have a mean of 0 and variance of 1. The following...