you'll want to please immediately change that path used here for my own system ( tqbsl%bub djfodf btu)jsftdtwto wherever you have installed the materials for this book ' not sure where you put itbut it' almost certainly not there we will use qboebt to read our csv inand create dataframe object out of it let' go ahead and run our codeand we can use the ifbe function on the dataframe to print out the first few lines and make sure that it looks like it makes sense egifbe vsffopvhixfibwftpnfwbmjeebubjouifpvuqvu sofor each candidate idwe have their years of past experiencewhether or not they were employedtheir number of previous employerstheir highest level of educationwhether they went to top-tier schooland whether they did an internshipand finally herein the hired columnthe answer where we knew that we either extended job offer to this person or not as usualmost of the work is just in massaging your datapreparing your databefore you actually run the algorithms on itand that' what we need to do here now scikit-learn requires everything to be numericalso we can' have ys and ns and bss and mss and phds we have to convert all those things to numbers for the decision tree model to work the way to do this is to use some short-hand in pandaswhich makes these things easy for examplee eg egnbq eg egnbq eg egnbq eg egnbq #   eg egnbq egifbe