Clean tabular dataset
You can’t train an experiment if the dataset has rows with empty values or malformed rows.
This makes sense - the model doesn’t know how to parse an empty value.
Remove rows with empty values
If you upload a csv file with empty values or malformed rows, the platform will help you clean the data set. The cleaning tool will remove rows with empty values in your input data.
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, it doesn’t matter if a row has empty values for these features, and you’ll keep as much data as possible. When 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.
Note: A deselected feature will be unavailable to use as input or target feature in an experiment.
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 to train the model, but the features will be unavailable as input or target features in your model.
Deselect the features you want to keep.
Click Apply changes to update and recalculate the dataset.
Use correct csv format
CSV files are known to have different formats depending on the tool you are working with. The Peltarion platform requires a specific format, so the tool will ensure the file complies with our requirements.