estimate the modelswe use randomly chosen observations out of , (or %)without showing the other of data to the model once the model is trainedwe predict the values for the other of the variables based on those for which we already know the true valueand calculate the model error with an error measure then we choose the model with the lowest error in this example we chose model because it has the lowest total error many models make strong assumptionssuch as independence of the inputsand you have to verify that these assumptions are indeed met this is called model diagnostics this section gave short introduction to the steps required to build valid model once you have working model you're ready to go to the last step step presenting findings and building applications on top of them after you've successfully analyzed the data and built well-performing modelyou're ready to present your findings to the world (figure this is an exciting partall your hours of hard work have paid off and you can explain what you found to the stakeholders data science process setting the research goal retrieving data data preparation data exploration data modeling presenting data presentation and automation automating data analysis figure step presentation and automation sometimes people get so excited about your work that you'll need to repeat it over and over again because they value the predictions of your models or the insights that you produced for this reasonyou need to automate your models this doesn' always mean that you have to redo all of your analysis all the time sometimes it' sufficient that you implement only the model scoringother times you might build an application that automatically updates reportsexcel spreadsheetsor powerpoint presentations the last stage of the data science process is where your soft skills will be most usefuland yesthey're extremely important in factwe recommend you find dedicated books and other information on the subject and work through thembecause why bother doing all this tough work if nobody listens to what you have to say