@MUJEEBUR RAHMAN Thanks, If your ML model has good performance metrics (you should decide which works best based on confusion matrix and which error can be handled and which should be reduced) then it is a great solution. Can you please share link to the model and the features that you are trying, also please share what feature engineering have you tried.
Could you please add more details about how you’re measuring your model error. If it’s doing worse than validation against your test set, then something weird is going on. If it’s doing better but not reaching 100% accuracy, that’s probably fine, and maybe preferable since it suggests that you’re not overfitting.