# Min-Max Normalization Min-max normalization is one of the most popular ways to normalize data.

For every feature,

• the minimum value of that feature gets transformed into a 0,
• the maximum value gets transformed into a 1
• and every other value gets transformed into a value between 0 and 1.

It is calculated by the following formula:

v’ is the new value of each entry in data.

v is the old value of each entry in data.

new_max(A), new_min(A) is the max and min value of the range (i.e boundary value of range required) respectively.

Where is the current value of feature F?

Let us consider one example to make the calculation method clear. Assume that the for the feature F ,

minimum value = \$50,000

maximum values = \$100,000

It needs to range F from 0 to 1.

In accordance with min-max normalization, v = \$80,000 is transformed to:

As you can see this technique enables us to interpret the data easily. There are no large numbers, only concise data that do not require further transformation and can be used in decision-making process immediately.

Min-max normalization has one fairly significant downside: it does not handle outliers very well. For example, if you have 99 values between 0 and 40, and one value is 100, then the 99 values will all be transformed to a value between 0 and 0.4.

That data is just as squished as before!

Take a look at the image below to see an example of this.

After normalizing, look at the below diagram it fixed the squishing problem on the y-axis, but the x-axis is still problematic. And the point in orange color is an outlier , which the min-max normalizer doesn’t handle.