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from sklearn.metrics import mean_squared_error

from sklearn.metrics import mean_squared_error

from xgboost import XGBRegressor

import numpy as np # linear algebra

from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(X_train, y_train)
preds = model.predict(X_valid)
print('RMAE: ', np.sqrt(mean_squared_error(y_valid, preds)))