from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.svm import SVR
from sklearn.datasets import load_diabetes

data = load_diabetes(as_frame=True)
df = data.frame

df_features = df.drop(labels=["target"], axis=1)
df_target = df.filter(items=["target"])

X_train, X_test, y_train, y_test = train_test_split(df_features, df_target["target"], shuffle=True, random_state=1)

regressor = SVR()
regressor.fit(X_train, y_train)

y_test_pred = regressor.predict(X_test)

mae = mean_absolute_error(y_test, y_test_pred)
rmse = mean_squared_error(y_test, y_test_pred, squared=False)

print("Mean Absolute Error: ", mae)
print("Root Mean Square Error: ", rmse)

Firstly, we are reading the dataset using the sklearn library and obtaining the DataFrame df. 

data = load_diabetes(as_frame=True)
df = data.frame

Now, we are splitting the dataset into features and target. df_features contain all the features of the dataset. And df_target contains the target variable in the “target” column.

df_features = df.drop(labels=["target"], axis=1)
df_target = df.filter(items=["target"])

Now, we are splitting the dataset into training and test set. Please note that the shuffle=True parameter indicates that the dataset is shuffled before the split. And the random_state=1 parameter controls the random number generator that is used for shuffling…