Analyzing the data

Now that the data is almost ready, we get to reap the rewards of our hard work by being able to do what some may consider magic – predict the future. However, our prediction is going to be even better than magic. Our prediction will be reliable, as it is driven by meaningful patterns within historical data.

Throughout this book, we have got to know three algorithms that can handle prediction: linear regression, multilayer perceptrons (MLPs), and decision trees.

To be able to see the applicability of the prediction models, we need to have a meaningful validation mechanism. We haven't covered this in this book, but there is a well-known and simple method normally called the hold-out mechanism or the train-test procedure. Simply put, a small part of the data will not be used in the training of the model, and instead, that small part will be used to evaluate how well the model makes predictions.

Specifically, in this case study, after removing the...