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.
Cet exercice fait partie du cours
Practicing Statistics Interview Questions in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de 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(____, ____)