Summary

In this chapter, we have seen how to use Jupyter Notebooks to perform parameter optimization and model selection.

We built upon the work we did in the previous chapter, where we trained predictive classification models for our binary problem and saw how decision boundaries are drawn for SVM, KNN, and Random Forest models. We improved on these simple models by using validation curves to optimize parameters and explored how dimensionality reduction can improve model performance as well.

Finally, at the end of the last exercise, we explored how the final model can be used in practice to make data-driven decisions. This demonstration connects our results back to the original business problem that inspired our modeling problem initially.

In the next chapter, we will depart from machine learning and focus on data acquisition instead. Specifically, we will discuss methods for extracting web data and learn about HTTP requests, web scraping with Python, and more data processing...