We explored several important areas in the world of supervised learning in this chapter. If you have followed this chapter from the beginning of our journey and braved your way till the end, give yourself a pat on the back! You now know what constitutes predictive analytics and some of the important concepts associated with it. Also, we have seen how predictive modeling works and the full predictive analytics pipeline in actual practice. This will enable you to build your own predictive models in the future and start deriving valuable insights from model predictions. We also saw how to actually use models to make predictions and evaluate these predictions to test model performance so that we can optimize the models further and then select the best model based on metrics as well and business requirements. Before we conclude and you start your own journey into predictive analytics, I will like to mention that you should always remember Occam's razor, which states that Among competing hypotheses, the one with the fewest assumptions should be selected, which can be also interpreted as Sometimes, the simplest solution is the best one. Do not blindly jump into building predictive models with the latest packages and techniques, because first you need to understand the problem you are solving and then start from the simplest implementation, which will often lead to better results than most complex solutions.