Training,Test data from ML

Python
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In any nearesr nighbour problem :

  1. We take a value from test set
  2. apply distance formula on this point in test set to every point in train set
    3)Take the minimum distance value from the test point and take this points index
  3. get the test point at which the distance is low with help of this index
  4. Get the label of the test point using train_label(which is the label data of training data)
    6)compare this label with test_label(which is part of test data)
  5. If there are equal it correct prediction and error rate lowers,if not error increases.

Are these steps correct,if not explain me please.

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