the data science process key values it also shows how two variables account for of the variation within the data set (component component %these variablescalled "component and "component ,are both combinations of the original variables they're the principal components of the underlying data structure if it isn' all that clear at this pointdon' worryprincipal components analysis (pcawill be explained more thoroughly in what you can also see is the presence of third (unknownvariable that splits the group of observations into two turning variables into dummies variables can be turned into dummy variables (figure dummy variables can only take two valuestrue( or false( they're used to indicate the absence of categorical effect that may explain the observation in this case you'll make separate columns for the classes stored in one variable and indicate it with if the class is present and otherwise an example is turning one column named weekdays into the columns monday through sunday you use an indicator to show if the observation was on mondayyou put on monday and elsewhere turning variables into dummies is technique that' used in modeling and is popular withbut not exclusive toeconomists in this section we introduced the third step in the data science process--cleaningtransformingand integrating data--which changes your raw data into usable input for the modeling phase the next step in the data science process is to get better customer year gender sales customer year sales male female figure turning variables into dummies is data transformation that breaks variable that has multiple classes into multiple variableseach having only two possible values or