The function below calls many of the helper functions outlined above to split the data, run the model, and output RMSE and MAE scores. We therefore can set up a base modeling structure that we will call for each model. Regressive Models: Linear Regression, Random Forest Regression, XGBoostįor our regressive models, we can use the fit-predict structure of the scikit-learn library. Score the models: this helper function will save the root mean squared error (RMSE) and mean absolute error (MAE) of our predictions to compare performance of our five models.Create a predictions data frame: generate a data frame that includes the actual sales captured in our test set and the predicted results from our model so that we can quantify our success.Reverse scaling: After running our models, we will use this helper function to reverse the scaling of step 2.Scale the data: using a min-max scaler, we will scale the data so that all of our variables fall within the range of -1 to 1. ![]()
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