print("\nl normalized data:\ "data_normalized_l the above line of code generates the following outputl normalized data[ - [- - - ] normalization it is also referred to as least squares this kind of normalization modifies the values so that the sum of the squares is always up to in each row it can be implemented on the input data with the help of the following python codenormalize data data_normalized_l preprocessing normalize(input_datanorm=' 'print("\nl normalized data:\ "data_normalized_l the above line of code will generate the following outputl normalized data[ - [- - - ]labeling the data we already know that data in certain format is necessary for machine learning algorithms another important requirement is that the data must be labelled properly before sending it as the input of machine learning algorithms for exampleif we talk about classificationthere are lot of labels on the data those labels are in the form of wordsnumbersetc functions related to machine learning in sklearn expect that the data must have number labels henceif the data is in other form then it must be converted to numbers this process of transforming the word labels into numerical form is called label encoding label encoding steps follow these steps for encoding the data labels in pythonstep importing the useful packages if we are using python then this would be first step for converting the data into certain formati preprocessing it can be done as follows