Python Data Structures and Algorithms

Min-max scalar

The min-max scalar form of normalization uses the mean and standard deviation to box all the data into a range lying between a certain min and max value. For most purposes, the range is set between 0 and 1. At other times, other ranges may be applied but the 0 to 1 range remains the default:

    scaled_values = preprocessing.MinMaxScaler(feature_range=(0,1))     results = scaled_values.fit(data).transform(data)     print(results) 

An instance of the MinMaxScaler class is created with the range (0,1) and passed to the variable scaled_values. The fit function is called to make the necessary calculations that will be used internally to change the dataset. The transform function effects the actual operation on the dataset, returning the ...