Summary
In this chapter, we have learned the fundamentals of Linear Regression.
After going through some basic mathematics, we dived into the mathematics of linear regression using one variable and multiple variables.
Challenges occurring with regression include loading data from external sources such as a .csv file, Yahoo Finance, or Quandl were dealt with. After loading the data, we learned how to identify the 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 engine, and how to predict the future. Our results were visualized by an easy-to-use Python graph plotting library called pyplot.
A more complex form of linear regression is a linear polynomial regression of arbitrary degree. We learned how to define these regression problems on multiple variables. We compared their performance to each other on stock price prediction problems. As an alternative to polynomial regression, we also...
