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Training accuracy is ~97% but validation accuracy is stuck at ~40%.

I am not aware of distinction between training data and validation process followed in data-science. Can not understand the meaning of two concepts and their purpose? A detailed explanation shall be appreciated

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A little more context about the data set and type of model would help, but most likely your model is overfitting to the training data. This means that your model is picking up noise from the training data and has basically "memorized" the data it has seen.

Therefore, the model is not generalizable, and this results in relatively poor performance on a data set it hasn't seen, explaining the 40% validation accuracy.

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    $\begingroup$ Be sure, I am a quack in datascience. Thanks for your response. I just had a reading of a question. AND it prompted me. I am trying to learn datascience/software. $\endgroup$
    – Subhash C. Davar
    Commented May 31, 2020 at 8:19
  • $\begingroup$ No worries, everyone has to start somewhere and this is a natural question people starting out in data science will have! And if my response was helpful, accepting the answer would be much appreciated: stackoverflow.com/help/someone-answers $\endgroup$
    – Derek O
    Commented May 31, 2020 at 8:21

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