Feature Scaling in Machine Learning using Python
In this tutorial we are going to learn how to do scaling the independent variable data using python. In data processing, it is also called as data normalization . What is the use of feature scaling in Machine Learning? The range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. For example, the majority of classifiers calculate the distance between two points by the Euclidean distance . If one of the features has a broad range of values, the distance will be governed by this particular feature. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance. Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it. We can do the feature scaling in many ways. As of now we are going to look the Min-M...