attack defense attack^ attack defense defense^ thus you can see that we have successfully obtained the necessary interaction features for the new dataset try building interaction features on three or more features nowbinning often when working with numeric datayou might come across features or attributes which depict raw measures such as values or frequencies in many casesoften the distributions of these attributes are skewed in the sense that some sets of values will occur lot and some will be very rare besides thatthere is also the added problem of varying range of these values suppose we are talking about song or video view counts in some casesthe view counts will be abnormally large and in some cases very small directly using these features in modeling might cause issues metrics like similarity measurescluster distancesregression coefficients and more might get adversely affected if we use raw numeric features having values which range across multiple orders of magnitude there are various ways to engineer features from these raw values so we can these issues these methods include transformationsscaling and binning/quantization in this sectionwe will talk about binning which is also known as quantization the operation of binning is used for transforming continuous numeric values into discrete ones these discrete numbers can be thought of as bins into which the raw values or numbers are binned or grouped into each bin represents specific degree of intensity and has specific range of values which must fall into that bin there are various ways of binning data which include fixed-width and adaptive binning specific techniques can be employed for each binning process we will use dataset extracted from the freecodecamp developer/coder survey which talks about various attributes pertaining to coders and software developers you can check it out yourself at dataset and take peek at some interesting attributes in [ ]fcc_survey_df pd read_csv('datasets/fcc_ _coder_survey_subset csv'encoding='utf- 'fcc_survey_df[['id ''employmentfield''age''income']head(figure - important attributes from the fcc coder survey dataset the dataframe depicted in figure - shows us some interesting attributes of the coder survey datasetsome of which we will be analyzing in this section the id variable is basically unique identifier for each coder/developer who took the survey and the other fields are pretty self-explanatory