Summary
In this chapter, we have learned the fundamentals of linear regression. After going through some basic mathematics, we looked at the mathematics of linear regression using one variable and multiple variables.
Then, we learned how to load external data from sources such as a CSV file, Yahoo Finance, and Quandl. After loading the data, we learned how to identify features and labels, how to scale data, and how to format data to perform regression.
We learned how to train and test a linear regression model, and how to predict the future. Our results were visualized by an easy-to-use Python graph plotting library called pyplot.
We also learned about a more complex form of linear regression: linear polynomial regression using arbitrary degrees. We learned how to define these regression problems on multiple variables and compare their performance on the Boston House Price dataset. As an alternative to polynomial regression, we also introduced SVMs as a regression model and...
