I have a dataset which consists of attributes on breakdown of machines.The target variable is machine status which are populated with ones and zeros. The distribution of ones and zeros are given below
0 - 19628 1 - 225
0 - signifies the machine is running good and 1 signifies there was a breakdown.
Now, should I go by splitting the dataset using scikit
train_test_split method ?. or introduce artificial rows to mitigate the tradeoff between ones and zeros and then split the dataset.
Well, What do I mean by artificial rows ? Populate some random data with having target variable as 1's But that would ultimately mislead the system. I don't see any other options or alternatives.
Is there any way how to make samples balanced?