Q:

why model is giving high accuracy of 84% but very low AUC 13%?

I have built model which gives me 84% accuracy for random forest and support vector machine but giving very low auc of 13% only. I am building this in python and I am new to machine learning and data science.
I am predicting 0 and 1 labels on dataset. My overall dataset is having records of 30744.
Label 1 - 6930
Label 0 - 23814
Could you please advise if this is fine? Is model is getting overfitted?
Appreciate any suggestion on improving auc? 

A:

Your problem appears to be that your dataset is unbalanced, and can indeed be overfitting.
For these cases, accuracy is not the best metric and the AUC is showing that probably your training could improve. It is difficult though to say that it's fine or not... have you tried more algos? Have you heard of other (better) results on this dataset?
Indeed, imagine a model supposed to predict whether there will be an eclipse tomorrow. A not-so-random forest can have a very high accuracy by simply saying "no" all the time. However, there won't be true positives at all!
A direct and simple test would be to pick a subsample from your dataset with the same number of examples from Label 0 and Labels 1. Your accuracy will then be a more relevant metric (and will probably be lower than the current 84%). In addition, you could try duplicating some Label 1 examples (eventually with small random perturbations) until the classes get balanced. By the way, be sure to keep separate parts of your dataset for train/val/test, and also consider cross-validation.
Finally, I suggest you to take a deeper look into dealing with unbalanced datasets, specially ROSE and SMOTE technique, and resampling in general.
These might be useful:

https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
https://shiring.github.io/machine_learning/2017/04/02/unbalanced
https://svds.com/learning-imbalanced-classes/

