Now we have the training set and the test set, let’s use scikit-learn to build the linear regression model!

The steps for multiple linear regression in scikit-learn are identical to the steps for simple linear regression. Just like simple linear regression, we need to import `LinearRegression`

from the `linear_model`

module:

from sklearn.linear_model import LinearRegression

Then, create a `LinearRegression`

model, and then fit it to your `x_train`

and `y_train`

data:

mlr = LinearRegression() mlr.fit(x_train, y_train) # finds the coefficients and the intercept value

We can also use the `.predict()`

function to pass in x-values. It returns the y-values that this plane would predict:

y_predicted = mlr.predict(x_test) # takes values calculated by `.fit()` and the `x` values, plugs them into the multiple linear regression equation, and calculates the predicted y values.

We will start by using two of these columns to teach you how to predict the values of the dependent variable, prices.

### Instructions

**1.**

Import `LinearRegression`

from scikit-learn’s `linear_model`

module.

**2.**

Create a Linear Regression model and call it `mlr`

.

Fit the model using `x_train`

and `y_train`

.

**3.**

Use the model to predict y-values from `x_test`

. Store the predictions in a variable called `y_predict`

.

Now we have:

`x_test`

`x_train`

`y_test`

`y_train`

- and
`y_predict`

!

**4.**

To see this model in action, let’s test it on Sonny’s apartment in Greenpoint, Brooklyn!

Or if you reside in New York, plug in your own apartment’s values and see if you are over or underpaying!