import seaborn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score, mean_squared_error
 
df = seaborn.load_dataset("mpg")
print(df.head())
print(df.info())
 
df = df.dropna()
X_train, X_test, y_train, y_test = train_test_split(df[["horsepower", "weight", "acceleration"]], df["mpg"],
                                                    train_size=0.8, shuffle=True, random_state=1)
ridge_regressor = Ridge()
ridge_regressor.fit(X_train, y_train)
 
y_test_predicted = ridge_regressor.predict(X_test)
 
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)

Here, we are first loading the “mpg” dataset using the seaborn library. After that, we print the first few lines of the dataset and information about the dataset. We get the following output:

    mpg  cylinders  displacement  ...  model_year  origin                       name
0  18.0          8         307.0  ...          70     usa  chevrolet chevelle malibu
1  15.0          8         350.0  ...          70     usa          buick skylark 320
2  18.0          8         318.0  ...          70     usa         plymouth satellite
3  16.0          8         304.0  ...          70     usa              amc rebel sst
4  17.0          8         302.0  ...          70     usa                ford torino
 
[5 rows x 9 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
#   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
0   mpg           398 non-null    float64
1   cylinders     398 non-null    int64  
2   displacement  398 non-null    float64
3   horsepower    392 non-null    float64
4   weight        398 non-null    int64  
5   acceleration  398 non-null    float64
6   model_year    398 non-null    int64  
7   origin        398 non-null    object 
8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB

Now, we can see the horsepower feature contains 392 non-null values out of 398 rows. So, we are using the dropna() function to drop the rows that contain null values…