Min Max Normalization Python and Matlab – Data Mining

By: Prof. Dr. Fazal Rehman | Last updated: March 3, 2022

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:

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

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

4. 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 )