# Test score bigger than Train score in Linear Regression

I'm new to ML and I'm trying to create a linear regression model. My data consist of 100 samples with 4 features each. This is my humble code

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=80)

reg = LinearRegression()
reg.fit(X_train, y_train)
score_test = reg.score(X_test, y_test)
score_train = reg.score(X_train, y_train)
print("Train Score is :", score_train)
print("Test Score is :", score_test)


The problem is that the score in Test set (0.97) is way bigger than the score in Train set (0.71). How we can explain this?