Summary

Using the knowledge from previous chapters, we started this chapter by performing an analysis of the Census Income dataset, with the objective of understanding the data that's available and making decisions about the pre-processing process. Three supervised learning classification algorithms—the Naïve Bayes algorithm, the Decision Tree algorithm, and the SVM algorithm—were explained, and were applied to the previously pre-processed dataset to create models that generalized to the training data. Finally, we compared the performance of the three models on the Census Income dataset by calculating the accuracy, precision, and recall on the different sets of data (training, validation, and testing).

In the next chapter, we will look at Artificial Neural Networks (ANNs), their different types, and their advantages and disadvantages. We will also use an ANN to solve the same data problem that was discussed in this chapter, as well as to compare its performance...