  We consider online algorithms for the {\em page migration problem} that use
predictions, potentially imperfect, to improve their performance. The best
known online algorithms for this problem, due to Westbrook'94 and Bienkowski et
al'17, have competitive ratios strictly bounded away from 1. In contrast, we
show that if the algorithm is given a prediction of the input sequence, then it
can achieve a competitive ratio that tends to $1$ as the prediction error rate
tends to $0$. Specifically, the competitive ratio is equal to $1+O(q)$, where
$q$ is the prediction error rate. We also design a ``fallback option'' that
ensures that the competitive ratio of the algorithm for {\em any} input
sequence is at most $O(1/q)$. Our result adds to the recent body of work that
uses machine learning to improve the performance of ``classic'' algorithms.
