ai with python data preparation we have already studied supervised as well as unsupervised machine learning algorithms these algorithms require formatted data to start the training process we must prepare or format data in certain way so that it can be supplied as an input to ml algorithms this focuses on data preparation for machine learning algorithms preprocessing the data in our daily lifewe deal with lots of data but this data is in raw form to provide the data as the input of machine learning algorithmswe need to convert it into meaningful data that is where data preprocessing comes into picture in other simple wordswe can say that before providing the data to the machine learning algorithms we need to preprocess the data data preprocessing steps follow these steps to preprocess the data in pythonstep importing the useful packagesif we are using python then this would be the first step for converting the data into certain formati preprocessing it can be done as followsimport numpy as np from sklearn import preprocessing here we have used the following two packagesnumpybasically numpy is 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 preprocessingthis package provides many common utility functions and transformer classes to change raw feature vectors into representation that is more suitable for machine learning algorithms step defining sample dataafter importing the packageswe need to define some sample data so that we can apply preprocessing techniques on that data we will now define the following sample datainput_data np array([ - ][- ][ - ][ - ]]