Summary

In this chapter, we created an end-to-end machine learning project. We started by studying some basic machine learning concepts to put us in sync. Then, we understood what was needed for the main goal of the project. First, we must understand the problem and know where we want to go so that the solution becomes clearer. In this case, our client was a digital marketing company that wanted to reduce the risk of their messages ending up in their spam filter, so we had to create a classification model to predict the probability of a message being marked as spam or not spam.

We loaded a dataset from UCI, which brought up some words and characters associated with spam messages and their percentage in the email. Then, we studied the data and created some visualizations to learn which elements were more likely to be classified as spam. Out of those, we created a new dataset with just six explanatory variables, reducing it from the original 57 columns.

Next, we trained and tested...