How to classify imbalanced data in weka?
Not sure about python but in the gui version you can use SpreadSubsample to reduce the class imbalance. If you feel that 'bad' is a a good representation of the class then you could experiment with different number of instances of 'good.'
To do this you need to select Filter ==> Supervised ==> Instance ==> SpreadSubsample ==> change the number of instances using 'max count'
I have an imbalanced training data and i am using logistic regression in weka to classify.

There are two classes good and bad. Good has 75000 instances and bad
  3000.  My test data has 10000 good data.

When i train it is more inclined to good data i.e it classifies almost all bad instances good. What should i do ?
I tried to have 10000 good instances in training data instead of 75000 but still the problem is same.