**Data**

- **Train data:** `data_train.csv` (Contains the features measured over 12 months for the individual business customers. You must generate meaningful features from this. Not everything could be measured for all business customers every month. You must therefore be able to deal with any NAs).
- **Target variable:** `Y_train.csv` (Categorical target variable, link these via the customer ID).
- **Test data:** `data_test.csv` (For model evaluation using unseen data.)
- The data is generated artificially, you do not have to worry about the meaning of the variables.

**Requirements**

- Generate useful features from the training data.
- Handle missing data (NA values).
- The model can be implemented in R or Python.
- Save your forecasts in a CSV file with the columns `ID` and `Y`
- **Example of submission:** `sample_submission.csv` (The submission contains the ID of the customer plus the probability of a recommendation (rec)).