Linear regression
In this exercise, you'll implement a simple linear regression model. Get ready to make predictions, visualize the model fit, and analyze the formula used to generate your fit.
By now, you're probably comfortable with the weather dataset that we'll be using. Your dependent variable will be the Humidity3pm feature. All of the standard packages have been imported for you.
This exercise is part of the course
Practicing Statistics Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.linear_model import LinearRegression
X = np.array(weather['Humidity9am']).reshape(-1,1)
y = weather['Humidity3pm']
# Create and fit your linear regression model
lm = ____
lm.fit(____, ____)