Feature scaling is a technique, performed in Pre-Processing stage,  that is done on the independent variables making the associated values of the particular variable  in a fixed range. Why we need 'Feature Scaling' ? Some machine learning models give more emphasis on the data of the independent variables over variables itself. That's why Scaling plays a very crucial role in such cases where we have to scale our data according some techniques( discussed later ). By applying Feature Scaling we are actually giving the same priority to all the independent variables present in the dataset. Machine learning models that are affected by Feature Scaling - Machine learning models or algorithms, involved in distance  measurement, are affected by the feature scaling. some of them are  KNN ( K-Nearest Neighbors ) [Used for Regression/Classification ] PCA ( Principal component Analysis ) [used for  Dimensionality reduction   ] K-means algorithm [used for  Clust...