  Machine Learning (ML) algorithms have been demonstrated to be capable of
predicting impact parameter in heavy-ion collisions from transport model
simulation events with perfect detector response. We extend the scope of ML
application to experimental data by incorporating realistic detector response
of the S$\pi$RIT Time Projection Chamber into the heavy-ion simulation events
generated from the UrQMD model to resemble experimental data. At 3 fm, the
predicted impact parameter is 2.8 fm if simulation events with perfect detector
is used for training and testing; 2.4 fm if detector response is included in
the training and testing, and 5.8 fm if ML algorithms trained with perfect
detector is applied to testing data that has included detector response. The
last result is not acceptable illustrating the importance of including the
detector response in developing the ML training algorithm. We also test the
model dependence by applying the algorithms trained on UrQMD model to simulated
events from four different transport models as well as using different input
parameters on UrQMD model. Using data from Sn+Sn collisions at E/A=270 MeV, the
ML determined impact parameters agree well with the experimentally determined
impact parameter using multiplicities, except in the very central and very
peripheral regions. ML selects central collision events better and allows
impact parameters determination beyond the sharp cutoff limit imposed by
experimental methods.
