from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
lr_model=LinearRegression()
lr_model.fit(X_train,y_train)
lr_predictions = lr_model.predict(X_test)
mae = mean_absolute_error(y_test, lr_predictions)
mae
16.029773809312484

from sklearn.ensemble import RandomForestRegressor
rf_model=RandomForestRegressor(n_estimators=150, max_depth=5, min_samples_split=5)
rf_model.fit(X_train,y_train)
rf_predictions = rf_model.predict(X_test)
mae = mean_absolute_error(y_test,rf_predictions)
mae
365.7303688636672