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:
- the
line_fitter.coef_
, which contains the slope - 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!
Instructions
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.