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We utilize machine learning methods to distinguish BL Lacertae objects (BLLac) from Flat Spectrum Radio Quasars (FSRQ) within a sample of likely X-rayblazar counterparts to Fermi 3FGL unassociated gamma-ray sources. From ourprevious work, we have extracted 84 sources that were classified as $\geq$ 99%likley to be blazars. We then utilize Swift$-$XRT, Fermi, and WISE (TheWide-field Infrared Survey Explorer) data together to distinguish the specifictype of blazar, FSRQs or BL Lacs. Various X-ray and Gamma-ray parameters can beused to differentiate between these subclasses. These are also known to occupydifferent parameter space on the WISE color-color diagram. Using all these datatogether would provide more robust results for the classified sources. Weutilized a Random Forest Classifier to calculate the probability for eachblazar to be associated with a BL Lac or an FSRQ. Based on P$_{bll}$, which isthe probability for each source to be a BL Lac, we placed our sources into fivedifferent categories based on this value as follows; P$_{bll}$ $\geq$ 99%:highly likely BL Lac, P$_{bll}$ $\geq$ 90%: likely BL Lac, P$_{bll}$ $\leq$ 1%:highly likely FSRQ, P$_{bll}$ $\leq$ 10%: likely FSRQ, and 90% $<$ P$_{bll}$$<$ 10%: ambiguous. Our results categorize the 84 blazar candidates as 50likely BL Lacs and the rest 34 being ambiguous. A small subset of these sourceshave been listed as associated sources in the most recent Fermi catalog, 4FGL,and in these cases our results are in agreement on the classification.

Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacs and FSRQs using Swift and WISE data