In one of our previous articles, we discussed polynomial regression using the sklearn Python library. We read the “mpg” dataset from the seaborn library and created a polynomial regression model that takes the horsepower of a car as input and determines its mpg or miles per gallon as output.

We also used the following Python code for that purpose.

import seaborn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import r2_score, mean_squared_error

df = seaborn.load_dataset("mpg")
print(df.head())
df = df.dropna()

X_train, X_test, y_train, y_test = train_test_split(df[["horsepower"]], df["mpg"], train_size=0.8, shuffle=True, random_state=1)
polynomial_features = PolynomialFeatures(degree=2, include_bias=False)
X_train_transformed = polynomial_features.fit_transform(X_train)

polynomial_regression = LinearRegression()
polynomial_regression.fit(X_train_transformed, y_train)

X_test_transformed = polynomial_features.transform(X_test)
y_test_predicted = polynomial_regression.predict(X_test_transformed)

r2 = r2_score(y_test, y_test_predicted)
rmse = mean_squared_error(y_test, y_test_predicted, squared=False)

print("R2: ", r2)
print("RMSE: ", rmse)

So, here, we are first creating additional features using the PolynomialFeatures class. Then, the features are passed to the …