feature engineering on numeric data numeric datafieldsvariablesor features typically represent data in the form of scalar information that denotes an observationrecordingor measurement of coursenumeric data can also be represented as vector of scalars where each specific entity in the vector is numeric data point in itself integers and floats are the most common and widely used numeric data types besides thisnumeric data is perhaps the easiest to process and is often used directly by machine learning models if you remember we have talked about numeric data previously in the "data descriptionsection in even though numeric data can be directly fed into machine learning modelsyou would still need to engineer features that are relevant to the scenarioproblemand domain before building model hence the need for feature engineering remains important aspects of numeric features include feature scale and distribution and you will observe some of these aspects in the examples in this section in some scenarioswe need to apply specific transformations to change the scale of numeric values and in other scenarios we need to change the overall distribution of the numeric valueslike transforming skewed distribution to normal distribution the code used for this section is available in the code files for this you can load feature_ engineering_numeric py directly and start running the examples or use the jupyter notebookfeature engineering on numeric data ipynbfor more interactive experience before we beginlet' load the following dependencies and configuration settings in [ ]import pandas as pd import matplotlib pyplot as plt import matplotlib as mpl import numpy as np import scipy stats as spstats %matplotlib inline mpl style reload_library(mpl style use('classic'mpl rcparams['figure facecolor'( mpl rcparams['figure figsize'[ mpl rcparams['figure dpi' now that we have the initial dependencies loadedlet' look at some ways to engineer features from numeric data in the following sections raw measures just like we mentioned earliernumeric features can be directly fed to machine learning models often since they are in format which can be easily understoodinterpretedand operated on raw measures typically indicated using numeric variables directly as features without any form of transformation or engineering typically these features can indicate values or counts