Using the knowledge from previous chapters, we started this chapter by performing an analysis on the Census Income Dataset, with the objective of understanding the data available and making decisions for the preprocessing 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 preprocessed 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 the ANN to solve the same data problem that was discussed here, and to compare its performance with that of the other supervised learning algorithms.