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<speak> Next, we will scale the data.<break strength="x-strong"/> We scale data by using a standard scaler.<break strength="x-strong"/> Standard Scaler performs the task of standardization.<break strength="x-strong"/> Usually, a data set contains variables that are different in scale.<break strength="x-strong"/> Our data set contains a Diabetes Pedigree Function column with values on a scale of 0 to 2.5.<break strength="x-strong"/> And an insulin column with values on a scale of 0 to 800.<break strength="x-strong"/> Most of the columns are different in scale.<break strength="x-strong"/> So we will standardize them with a common scale from 0 to 1. <break strength="x-strong"/> We are using standard scalar for standardizing values.<break strength="x-strong"/> We are creating an instance of the standard scaler.<break strength="x-strong"/> We are transforming only features, not the output variable. <break strength="x-strong"/>So, we are dropping the outcome column because it's our label.<break strength="x-strong"/> We are applying standard scaler on all features.<break strength="x-strong"/> We are doing standard scaling to all these columns,<break strength="x-strong"/> and we are checking our modified data frame. <break strength="x-strong"/> We are storing modified data frame in X. <break strength="x-strong"/> Let's run the cell.  <break strength="x-strong"/> So these are all our features. <break strength="x-strong"/> </speak>