Clean tabular dataset

You can’t train an experiment if the dataset has rows with empty values or malformed rows. The platform includes a tool that will clean your tabular dataset and make it usable.

Remove rows with empty values

The input dataset can’t contain any blank values. This makes sense - the model doesn’t know how to parse a blank value. The cleaning tool will remove rows with empty values in your input data.

Remove empty rows

Don’t clean too much - keep as much data as you can

Deselect the features that you won’t need in your experiment. This way, you keep as much data as possible.

When you’re working with deep learning, you want to train on as much data as possible. That’s why it’s a good idea to keep as much data as possible.

Example:
Store name, Advertising level, or Holiday is of no interest in your experiment. If you deselect these features, then it doesn’t matter if a row has an empty value for these features. The row will be kept for use in the experiment.

Deselect features to keep data

Correct csv format

CSV files are known to have different formats depending on the tool you are working with. The Peltarion platform requires a certain format, so the tool will make sure the file complies with our requirements.

Clean dataset before upload

If you want to check and clean your data before upload, you can use our DataCleaner tool.

Peltarion Platform DataCleaner tool
Was this page helpful?
YesNo