Hands-On Recommendation Systems with Python

Regression

k-NN regression works in almost the same way. Instead of classes, we compute the property values of the k-NN.

Imagine that we have a housing dataset and we're trying to predict the price of a house. The price of a particular house will therefore be determined by the average of the prices of the houses of its k nearest neighbors. As with classification, the final target value may differ depending on the value of k.

For the rest of the algorithms in this section, we will go through only the classification process. However, just like k-NN, most algorithms require only very slight modifications to be suitable for use in a regression problem.