**Feature scaling** is the task of taking a parameter and rescale it to any predefined interval. If this is done with all parameters it can be said that the data set is **normalized**. After the feature scaling the internal structure of the data is kept, meaning that if one value was much bigger than some other value this** proportion is persistent** after the feature scaling.

By using the **min **and **max **value of all seen examples of a variable we can create a sense of whether this is the biggest value seen (100%) or the smallest value seen (0%). If we think of this as percentage we can say that the features have been scaled between 0 and 100. The interval of the scaling can be different depending of what you are trying to achieve but the most **common scenario is to scale features [0, 1].** In this sense it can also be thought of as a percentage in decimal form.

There are different type of feature scalings but a very common is the **min/max scaling **that we have previously discussed. The new value x-prime is givenĀ by subtracting the min value of x over the interval of x (given by taking the max value of x minus the min value of x). Other common data normalization techniques include the mean normalization.

Here, the x-prime is instead given by how much from the x value is deviating from the mean. If x is** 20% less than the mean** this normalization will yield -0.2 while if x is** 20% more than the mean** it will yield 0.2. Therefore this normalization will have the **domain [-1, 1].**