## Intro

Linear Regression is the most common starting point for Machine Learning models.
In general, linear regression helps model a relation ship between a x or multiple x variables
and a y. For example, we may want to predict housing prices (y) based on different properties (x's)
of the houses (size, location, etc). In this article, we will see how to create
a simple linear regression model using Sklearn.

## Creating a Linear Regression Model

To create a Linear Regression model, we use the `linear_model.LinearRegression`

clss from Sklearn.
We start by creating an instance of the class, then supply and X (or X's) and a Y (the target)
to the `fit`

method. This will create a linear model (equation) for us. Once we have the fit model
we can run predictions and score the model to see how well it performs.

```
import numpy as np
from sklearn import datasets, linear_model
x, y = datasets.load_diabetes(return_X_y=True)
x = x[:, np.newaxis, 2]
model = linear_model.LinearRegression()
model.fit(x, y)
print(model.score())
```