Congratulations! You’ve now built a linear regression algorithm from scratch.

Luckily, we don’t have to do this every time we want to use linear regression. We can use Python’s scikit-learn library. Scikit-learn, or sklearn, is used specifically for Machine Learning. Inside the linear_model module, there is a LinearRegression() function we can use:

from sklearn.linear_model import LinearRegression

You can first create a LinearRegression model, and then fit it to your x and y data:

line_fitter = LinearRegression()
line_fitter.fit(X, y)

The .fit() method gives the model two variables that are useful to us:

1. the line_fitter.coef_, which contains the slope
2. the line_fitter.intercept_, which contains the intercept

We can also use the .predict() function to pass in x-values and receive the y-values that this line would predict:

y_predicted = line_fitter.predict(X)

Note: the num_iterations and the learning_rate that you learned about in your own implementation have default values within scikit-learn, so you don’t need to worry about setting them specifically!