# How to handle multi-label feature for binary classification problem?

I have dataset like :

   profile     category  target
0        1      [5, 10]       1
1        2          [1]       0
2        3   [23, 5000]       1
3        4  [700, 4500]       0


How to handle category feature, this table may have others additional features too. One hot encoding lead to consume too much space.because number of rows is around 10 million. Any suggestion would be helpful.

I recommend using scikit-learn's MultiLabelBinarizer to encode your category feature. This is the multi-label analog of one-hot encoding, but it has a sparse_output mode, which should solve your problem of limited memory.