Min Max is a data normalization technique like Z score, decimal scaling, and normalization with standard deviation. It helps to normalize the data. It will scale the data between 0 and 1. This normalization helps us to understand the data easily. For example, if I say you to tell me the difference between 200 and 1000 then it’s a little bit confusing as compared to when I ask you to tell me the difference between 0.2 and 1.

Min Max normalization formula

Min: The minimum value of the given attribute. Here Min is 8 Max: The maximum value of the given attribute. Here Max is 20 V: V is the respective value of the attribute. For example here V1=8, V2=10, V3=15, and V4=20 newMax: 1 newMin: 0  

Min max normalization example

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Example #2

Normalize the following data; After Normalization:

1. Find the minimum and maximum values for each attribute:
   • Programming: min = 44, max = 55
   • Database: min = 33, max = 56
   • Stats: min = 4, max = 34
   • Data Mining: min = 33, max = 55
2. Apply the min-max normalization formula for each value:
   • For the first row, Rollno is not normalized, so we leave it as it is.
   • For Programming in the first row: (55 – 44) / (55 – 44) = 0.6667
   • For Database in the first row: (33 – 33) / (56 – 33) = 0.0000
   • For Stats in the first row: (4 – 4) / (34 – 4) = 0.0000
   • For Data Mining in the first row: (55 – 33) / (55 – 33) = 1.0000
   • For the second row, we repeat the same process.

Comparison of Min-Max Normalization and Z-Score Normalization

Let’s see the comparison of Min-Max Normalization and Z-Score Normalization