text_plain_output_1.png
from sklearn.metrics import mean_squared_log_error

model = LinearRegression(iterations=10000, learning_rate=1e-09)
model.fit(X_train, y_train)
mean_squared_log_error(y_test, model.predict(X_test))

scaler = StandardScaler()
scaler.fit(X_train)
X_train, X_test = (scaler.transform(X_train), scaler.transform(X_test))
model = LinearRegression(iterations=10000, learning_rate=0.1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mean_squared_log_error(y_test, y_pred)