Introduction

In the previous chapter, we covered the key steps involved in working with a supervised learning data problem. Those steps aim to create high-performing algorithms, as explained in the previous chapter.

This chapter focuses on applying different algorithms to a real-life dataset, with the underlying objective of applying the steps that we learned previously to choose the best-performing algorithm for the case study. Considering this, you will pre-process and analyze a dataset, and then create three models using different algorithms. These models will be compared to one another in order to measure their performance.

The Census Income dataset that we'll be using contains demographical and financial information, which can be used to try and predict the level of income of an individual. By creating a model capable of predicting this outcome for new observations, it will be possible to determine whether a person can be pre-approved to receive a loan.