These days machine learning is all the hype. Unfortunately, these algorithms are usually considered rather hard to interpret, leaving business stakeholders feeling queasy. I've seen analytics teams use these powerful tools to build exceptionally good models only to have them thrown in the scrap heap. People just didn't get them. And if they don't get them, they don't trust them.

However, just like a biological neuron only fires when a certain treshold is exceeded, the artificial neuron will also only fire when the sum of the inputs exceeds a treshold, let's say for example 0. For this tutorial, you'll use the wine quality data set that you can find in the wine quality data set from the UCI Machine Learning Repository. You might already know this data set, as it's one of the most popular data sets to get started on learning how to work out machine learning problems. One of the first things that you'll probably want to do is to start off with getting a quick view on both of your DataFrames: Now is the time to check whether your import was successful: double check whether the data contains all the variables that the data description file of the UCI Machine Learning Repository promised you.