Summary

In this chapter, we took our first big leap into constructing machine learning models and making predictions with labeled datasets. We began our analysis by looking at a variety of different ways to construct linear models, starting with the precise least squares method, which is very good when modeling small amounts of data that can be processed using the available computer memory. The performance of our vanilla linear model was improved using dummy variables, which we created from categorical variables, adding additional features and context to the model. We then used linear regression analysis with a parabolic model to further improve performance, fitting a more natural curve to the dataset. We also implemented the gradient descent algorithm, which we noticed, while not as precise as the least squares method was for our limited dataset, was most powerful when the dataset cannot be processed on the resources available on the system.

Finally, we investigated the use of autoregression...