<s>[INST] # Predicting the Future using Historical Data
This chapter presents one of the most important techniques in data science: using historical data to make predictions about the future. We will do this through a combination of regression and natural language.
There are many tools that claim to be able to predict the future. Astrology, prophecy, and tarot cards are all popular methods that have been used for centuries.1 However, these methods have a problem: they do not actually _work_.2 The scientific community has been unable to verify the accuracy of these methods, and they are not reliably able to make predictions about the future.3
Footnote 1: If you’re interested in learning more about the history of these methods, I recommend _The Oxford Handbook of the History of Astrology_.
Footnote 2: If you are interested in exploring this idea, I recommend _The Forer Effect and the Forer Profile_, which describes the experiment that proved the methods do not work.
Footnote 3: It’s worth noting that there are some obvious exceptions—astrology, for example, was an early form of astronomy. Many of the early astronomers were also astrologers, as they were able to use their knowledge of the stars to make predictions about future celestial events.
In this chapter, we will be focusing on one of the most powerful tools for predicting the future: _regression_. Regression, for our purposes, is a statistical technique that allows us to examine the relationship between two variables and make predictions about the future based on that relationship.
In this chapter, we will use regression to predict the future price of Bitcoin. We will explore the relationship between the price of Bitcoin and external factors, learning how to build and interpret a regression model, and then use it to make predictions about the future price of Bitcoin.
What else can we use regression for?[/INST]