So far, you have worked with the ‘**statsmodels**‘ package. This is a great package if you want to fit a line and draw inferences as well. But many times, you may not be interested in the statistics part of linear regression. You might just want to fit a line through the data and make predictions. In such cases, you can use **‘SKLearn’**, which involves lesser hassle than ‘stats models’. Also, the industry standard as to what package should be used varies widely. Some companies prefer stats models whereas some others prefer **SKLearn**, so it is better for you if you know about both of these packages.

SKLearn is a ‘lighter’ version of the linear regression packages. The two simple steps to fit a line using SKLearn are as follows:

PowerShell

```
from sklearn.linear_model import LinearRegression
lm = LinearRegression() # Create a linear regression object
lm.fit(X_train, y_train)
```