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!



We have imported a dataset of soup sales data vs temperature.

Run the code to see the scatterplot. Can you envision the line that would fit this data?


Create an sklearn linear regression model and call it line_fitter.


Fit the line_fitter object to temperature and sales.


Create a list called sales_predict that is the predicted sales values that line_fitter would generate from the temperature list.


Plot sales_predict against temperature as a line, on the same plot as the scatterplot.

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