  The main goal of The Extreme Universe Space Observatory on a Super Pressure
Balloon (EUSO-SPB1) was to observe from above extensive air showers caused by
ultra-high energy cosmic rays. EUSO-SPB1 uses a fluorescence detector that
observes the atmosphere in a nadir observation mode from a near space altitude.
During the 12-day flight, an onboard first level trigger detected more than
\num{175000} candidate events. This paper presents an approach to recognize air
showers in this dataset. The approach uses a feature extraction method to
create a simpler representation of an event and then it uses established
machine learning techniques to classify data into at least two classes - shower
and noise. The machine learning models are trained on a set of air shower
simulations put on top of the background observed during the flight and a set
of events from the flight. We present the efficiency of the method on datasets
of simulated events. The flight data events are also used in unsupervised
learning methods to identify groups of events with similar features. The
presented methods allow us to shorten the candidate events list and, thanks to
the groups of similar events identified by the unsupervised methods, the
classification of the triggered events is made simpler.
