by lashun height

Version 1 (October 29, 2018)

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import numpy as np

sklearn import preprocessing

Here we have used the following two packages −

· NumPy − Basically NumPy is a general purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

· Sklearn.preprocessing − This package provides many common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for machine learning algorithms.

Step 2 − Defining sample data − After importing the packages, we need to define some sample data so that we can apply preprocessing techniques on that data. We will now define the following sample data −

Input_data = np.array([2.1, -1.9, 5.5],

[-1.5, 2.4, 3.5],

[0.5, -7.9, 5.6],

[5.9, 2.3, -5.8]])

Step3 − Applying preprocessing technique − In this step, we need to apply any of the preprocessing techniques.

The following section describes the data preprocessing techniques.

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