## Min Max Normalization Python and Matlab – Data Mining

Min Max Normalization in Python and Matlab is today topic of discussion in this tutorial. Min-Max normalization is very helpful in data mining, mathematics, and statistics. Hopefully, you will get benefit from this.

## Min Max Normalization Equation

## Data Before and After Normalization

Let’s see in the figure, the data before and after min-max normalization.

## Min Max Normalization **Python Source Code**

Lets see the source code of Min Max Normalization in Python.

**def **__normalize(self , data ) :

*# Save the Real shape of the Given Data
*shape = data.shape

*# Smoothing the Given Data Valuesto 1 dimension*

data = np.reshape( data , (-1 , ) )

*# Find MinValue and MaxValue*

MaxValue = np.max( data )

MinValue = np.min( data )

*# Normalized values are store in a newly created array*

normalized_values = list()

*# Iterate through every value in data*

**for**

**AttributeValue in the given data:**

*# Normalize*

AttributeValue_normalized = (AttributeValue – MinValue ) / ( MaxValue – MinValue )

*# Append it in the array*

normalized_values.append( AttributeValue_normalized )

*# Convert to numpy array*

n_array = np.array( normalized_values )

*# Reshape the array to its Real shape and return it.*

**return**np.reshape( n_array , shape )

**Explanation of the code**

* # Save the Real shape of the Given Data
*shape = data.shape

*# Smoothing the Given Data Values to 1 dimension*

data = np.reshape( data , (-1 , ) )

**Some further steps:**

- We need to Save the Real shape of the data.
- We need to smooth the given data.
- The data is reshaped to a single-dimension.

*# Find MinValue and MaxValue
*MaxValue = np.max( data )

MinValue = np.min( data )

- Then, we find the MinValue and MaxValue of the data.

normalized_values = list()

*# Iterate through every value in data
*

**for**

**AttributeValue in the given data:**

*# Normalize*

AttributeValue_normalized = (AttributeValue – MinValue ) / ( MaxValue – MinValue )

*# Append it in the array*

normalized_values.append( AttributeValue_normalized )

5. After normalization, we can Save it in the normalized_values list.

*# Convert to numpy array
*n_array = np.array( normalized_values )

*# Reshape the array to its Real shape and return it.*

**return**np.reshape( n_array , shape )

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