Data Normalization Calculator Formula
Understand the math behind the data normalization calculator. Each variable explained with a worked example.
Formulas Used
Normalized Value [0,1]
normalized = (value - min_val) / (max_val - min_val)Normalized (%)
normalized_pct = ((value - min_val) / (max_val - min_val)) * 100Data Range
range_val = max_val - min_valVariables
| Variable | Description | Default |
|---|---|---|
value | Value to Normalize | 75 |
min_val | Dataset Minimum | 20 |
max_val | Dataset Maximum | 100 |
How It Works
How to Normalize Data (Min-Max)
Formula
Normalized = (X - Min) / (Max - Min)
Min-max normalization rescales data to the [0, 1] interval. The minimum maps to 0 and the maximum maps to 1. This is useful when features have different scales and you need them comparable, such as in machine learning preprocessing.
Worked Example
Dataset ranges from 20 to 100. Normalize the value 75.
- 01Range = 100 - 20 = 80
- 02Normalized = (75 - 20) / 80 = 55 / 80 = 0.6875
- 0375 is 68.75% of the way from min to max
Frequently Asked Questions
What if a new value falls outside the original min-max range?
The normalized value will be below 0 or above 1. This can happen with new data. You can clip to [0,1] or recalculate min and max. This sensitivity to outliers is a drawback of min-max normalization.
When should I use normalization vs. standardization?
Use min-max normalization when you need bounded [0,1] values or when the data does not follow a normal distribution. Use z-score standardization when you want mean=0, SD=1 and the data is roughly normal. Neural networks often prefer [0,1] inputs.
Does normalization change the distribution shape?
No. Min-max normalization is a linear transformation, so it preserves the relative distances and distribution shape. It only shifts and scales the data.
Ready to run the numbers?
Open Data Normalization Calculator