The performance of the models will be evaluated using the Mean Absolute Percentage Error (MAPE) metric, which provides a clear and understandable measure of forecast accuracy.

The Mean Absolute Percentage Error (MAPE) is a measure of prediction accuracy in a forecasting model. It calculates the average of the absolute percentage errors of the forecasts. The formula for MAPE is as follows:

\[ \text{MAPE} = \frac{100\%}{n} \sum_{i=1}^{n} \left| \frac{y_i - \hat{y}_i}{y_i} \right| \]

where:
- \( n \) is the total number of observations,
- \( y_i \) is the actual value of the observation,
- \( \hat{y}_i \) is the predicted value.

The MAPE gives a simple percentage which makes it easy to interpret and understand. A lower MAPE value indicates better predictive accuracy of the model.

## Submission File

For each `id` in the test set, you must predict a value for the `umsatz` variable. The file should contain a header and be formatted as follows:
```
id,umsatz
201808011,0.0
201808012,0.0
201808013,0.0
```