The transaction-rate bottleneck built into popular proof-of-work (PoW)-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this article, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions.