Using regression analysis to predict house prices
In the previous chapter, we examined the first of the two supervised learning methods in the Elastic Stack – classification. The goal of classification analysis is to use a labeled dataset to train a model that can predict a class label for a previously unseen datapoint. For example, we could train a model on historical measurements of cell samples coupled with information about whether or not the cell was malignant and use this to predict the malignancy of previously unseen cells. In classification, the class or dependent variable that we are interested in predicting is always a discrete quantity. In regression, on the other hand, we are interested in predicting a continuous variable.
Before we examine the theoretical underpinnings of regression a bit closer, let's dive right in and do a practical walk-through of how to train a regression model in Elasticsearch. The dataset we will be using is available on Kaggle (https...
