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The data collected was quite less when compared to the data required for ANN. The less data accounts for the less accuracy generated by the model. We fail to consider higher number of hidden layers since there is a chance of over fitting. The main reason to generate the graphs of accuracy vs. epochs is to see how the accuracy is changing with respect to epochs. The main reason to generate the graph of accuracy vs. hidden layer is to see which number of hidden layers is best to make the prediction. This model can be very much helpful in the software industries to make predictions on how much stress a person would face if he is to do a coding part of a project. So, the company can give the required instruction to the employee and suggest some methods which can increase the skill level of the person which reduce the mental work load while he does the project and also improving the capacity of the employee. We can also use this in the universities to see how much stress a student faces when he tries to solve programming questions. Using this data, the university officials can tweak their teaching policies and try to improvise the skill level of the student and help in improving the new generation day by day.