## Intro

Linear regression is the most common introduction to machine learning. The basic model fits a straight line. It assume a direct relationship. For example, you can have a list of heights and weight. If you assume the taller people are, the heavier they are, this would be a stright line/linear relationship. In this article, we will learn how to build a basic linear regression model with Sklearn.

The assumption is good, but doesn't capture everything. Modified linear regression models can help with these variations. We will look at these in the future.

## Using Linear Regression

To build (fit) a linear regression model we use the `LinearRegression`

class. We create an instance of the class, then we use the `fit`

method. We pass the features (x) and target (y) to this method. The `fit`

function then returns a model that is our linear regression model.

```
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
boston = load_boston()
features = boston.data
target = boston.target
regression = LinearRegression()
model = regression.fit(features, target)
```