At the beginning of this chapter, you learned about simple linear regression analysis to model the relationship between a single explanatory variable and a continuous response variable. We then discussed a useful explanatory data analysis technique to look at patterns and anomalies in data, which is an important first step in predictive modeling tasks.

We built our first model by implementing linear regression using a gradient-based optimization approach. We then saw how to utilize scikit-learn's linear models for regression and also implement a robust regression technique (RANSAC) as an approach for dealing with outliers. To assess the predictive performance of regression models, we computed the mean sum of squared errors and the related metric. Furthermore, we also discussed a useful graphical approach to diagnose problems of regression models: the residual plot.

After we discussed how regularization can be applied to regression models to reduce the model complexity and avoid overfitting, we also introduced several approaches to model nonlinear relationships including polynomial feature transformation and random forest regressors.

We have discussed supervised learning, classification, and regression analysis in great detail throughout the previous chapters. In the next chapter, we are going to learn about another interesting subfield of machine learning, unsupervised learning and also we will learn how to use cluster analysis for finding hidden structures in data in the absence of target variables.