Summary
In this chapter, we learned about regression, the least-squares method, and line, curve, and surface fitting. We also learned about how to apply these methods to a real-world dataset and how to predict the values for an output variable (Y) given access to some historical dataset that has both X and Y values. Caution should be taken if we are trying to extrapolate outside the range of X values for a dataset; the predicted values might not be reliable. You should now be able to apply these concepts to your own datasets and use Python libraries such as SciPy, NumPy, and scikit-learn to carry out regression analysis and prediction.
In the next chapter, we will learn about web searches from both mathematical and practical perspectives. We will also look at Google's PageRank algorithm and discuss the linear algebra involved.
