even though ( |ais high ( %)it doesn' mean ( |bis high people overlook this all the timeso if there' one lesson to be learned from bayestheoremit is to always take these sorts of things with grain of salt apply bayestheorem to these actual problems and you'll often find that what sounds like high accuracy rate can actually be yielding very misleading results if you're dealing with low overall incidence of given problem we see the same thing in cancer screening and other sorts of medical screening as well that' very real problemthere' lot of people getting veryvery real and very unnecessary surgery as result of not understanding bayestheorem if you're going into the medical profession with big datapleasepleaseplease remember this theorem so that' bayestheorem always remember that the probability of something given something else is not the same thing as the other way aroundand it actually depends lot on the base probabilities of both of those two things that you're measuring it' very important thing to keep in mindand always look at your results with that in mind bayestheorem gives you the tools to quantify that effect hope it proves useful summary in this we talked about plotting and graphing your data and how to make your graphs look pretty using the nbuqmpumjc library in python we also walked through the concepts of covariance and correlation we looked at some examples and figured out covariance and correlation using python we analyzed the concept of conditional probability and saw some examples to understand it better finallywe saw bayestheorem and its importanceespecially in the medical field in the next we'll talk about predictive models