Summary

In this chapter, you explored a new approach to machine learning, that is, supervised machine learning, and saw how it can help a business make predictions. These predictions come from models that the algorithm learns. The models are essentially mathematical expressions of the relationship between the predictor variables and the target. You learned about linear regression – a simple, interpretable, and therefore powerful tool for businesses to predict quantities. You saw that feature engineering and data cleanup play an important role in the process of predictive modeling and then built and interpreted your linear regression models using scikit-learn. In this chapter, you also used some rudimentary approaches to evaluate the performance of the model. Linear regression is an extremely useful and interpretable technique, but it has its drawbacks.

In the next chapter, you will expand your repertoire to include more approaches to predicting quantities and will explore...