Hey Scientists,

So i have partaken in a Kaggle competition (my first) but I'm stuck at a point and need your help!
The problem statement is of binary classification and i have test & train dataset with almost 20 features.
But the end submission or prediction set has only Householdkeys against which i need to predict 1or 0, and the f1 score on that would result a winner. My question is how can i train a model on 20+ features and then predict on only 1??

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The submission will have the index(prefer...) of your test/validation set. So you need to put your classification output against each key/index

I have checked the no. Of unique household keys are same but no. Of rows varies by a lot :(

What it means by houshold keys? If validation set is there you need to get F1 score on that set.

So that third file is only household keys not features in it. It is ok those keys are not present in train and test but It should be having features in it. Is there any other submission?

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