Summary

In this chapter, we used several machine learning algorithms, some of them in R and Python to compare and contrast. We used naive Bayes to determine how the data might be used. We applied nearest neighbor in a couple of different ways to see our results. We used decision trees to come up with an algorithm for predicting. We tried to use neural network to explain housing prices. Finally, we used the random forest algorithm to do the same—with the best results!
In the next chapter, we will look at optimizing Jupyter notebooks.