Predicting continuous values using linear regression

At the risk of stating the obvious, linear regression aims to find the relationship between an output (y) based on an input (x) using a mathematical model that is linear to the input variables. The output variable, y, is a continuous numerical value. If we have more than one input/explanatory variable (x), as in the example that we are going to see, we call it multiple linear regression. The dataset that we'll use for this recipe, for lack of creativity, is lifted from the UCI website at http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/. This dataset has 1599 instances of various red wines, their chemical composition, and their quality. We'll use it to predict the quality of a red wine.

How to do it...

Let's summarize the steps:

1. Importing the data.

2. Converting each instance into a LabeledPoint.

3. Preparing the training and test data.

4. Scaling the features.

5. Training the model.

6. Predicting against the test data.

7. Evaluating the model...