Using k-means clustering to label regression data

In this section, we are going to use the unsupervised K-means clustering method to label the regression data. We use K-means to cluster data points into groups or clusters based on their similarity.

Once the clustering is done, we can compute the average label value for each cluster by taking the mean of the labeled data points that belong to that cluster. This is because the labeled data points in a cluster are likely to have similar label values since they are similar in terms of their feature values.

Figure 3.6 – Basic k-means clustering with no. of clusters =3

For example, let’s say we have a dataset of house prices with which we want to predict the price of a house based on features such as size, location, number of rooms, and so on. We have some labeled data points that consist of the features and their corresponding prices, but we also have some unlabeled data points with the same...