High accuracy on test-set, what could go wrong?
There are many possible ways to make errors giving you a really huge score on test in Data Science. Here are a few examples :

Your test set is also in your train set : Imagine you have data from 2010 to 2020, and use 2019 as test. If you trained on all 2010-2020 without excluding 2019, then you'll test on a data well known by the model, since it was used to train it. Moreover, if the model tends to overfit (so fits &quot;too perfectly and precisely&quot; with training set), you could achieve a 99% accuracy

Data leakage : This is a phenomenom in which your test set contains info that you shouldn't have in real new cases. Example : you're using Titanic dataset, predicting who dies and who survives. Imagine now the dataset has an attribute called &quot;Hour of death&quot;, empty if the person survived, and filled with an hour if he died. Then your model will just learn &quot;if this attribute is empty then the person survive, else he died&quot;. On your test set, you'll apply your model, knowing this info that you shouldn't know if you had to predict true new cases


Wathever happens, a 99% accuracy have to make you wonder and look for errors, this is almost impossible to achieve unless your problem is REALLY easy (and might not need a Data Science model at all)