Day trading, as a speculative trading style that involves the opening and closing of a position on a daily basis, can be affected by all sorts of variations in the market. It is desirable to build a model to predict whether a transaction can benefit at all, given the entry time, the stock information, and real-time market situations. We implemented different supervised learning models for that purpose, with a substantial amount of minute-level trading opportunity data for US stocks. This paper describes data preprocessing, modeling methodologies, comparison and evaluation of several classifiers, and further improvement and insights into the modeling result. The overall performances of all models do not differ significantly, but certain models may be recommended based on different risk-return preferences according to our performance analysis.