  Approximately one-third of the gamma-ray sources in the third Fermi-LAT
catalog are unidentified or unassociated with objects at other wavelengths.
Observations with Swift-XRT have yielded possible counterparts in $\sim$30% of
these source regions. The objective of this work is to identify the nature of
these possible counterparts, utilizing their gamma ray properties coupled with
the Swift derived X-ray properties. The majority of the known sources in the
Fermi catalogs are blazars, which constitute the bulk of the extragalactic
gamma-ray source population. The galactic population on the other hand is
dominated by pulsars. Blazars and pulsars occupy different parameter space when
X-ray fluxes are compared with various gamma-ray properties. In this work, we
utilize the X-ray observations performed with the Swift-XRT for the unknown
Fermi sources and compare their X-ray and gamma-ray properties to differentiate
between the two source classes. We employ two machine learning algorithms,
decision tree and random forest classifier, to our high signal-to-noise ratio
sample of 217 sources, each of which correspond to Fermi unassociated regions.
The accuracy score for both methods were found to be 97% and 99%, respectively.
The random forest classifier, which is based on the application of a multitude
of decision trees, associated a probability value (P$_{bzr}$) for each source
to be a blazar. This yielded 173 blazar candidates with P$_{bzr}$ $\geq$ 90%
for each of these sources, and 134 of these possible blazar source associations
had P$_{bzr}$ $\geq$ 99%. The results yielded 13 sources with P$_{bzr}$ $\leq$
10%, which we deemed as reasonable candidates for pulsars, 7 of which result
with P$_{bzr}$ $\leq$ 1%. There were 31 sources that exhibited intermediate
probabilities and were termed ambiguous due to their unclear characterization
as a pulsar or a blazar.
