Different accuracy for different rng values
The training errors in this dataset has a huge difference (99% vs 57%). So, maybe the one with the rng(1) split has overfitted your dataset.


  So there is a huge change in accuracy as visible. What does this mean?
  Am I training it wrong?


The huge change might be due to overfitting. (Also, judge the model through validation curves, and then fit a model which balances the bias-variance plot.)