Supernova ( SN ) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope ( LSST ) , given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe . We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm . Our classifier results in an AUC of 0.98 which represents excellent classification . We are able to obtain a photometric SN sample containing 99 % SNe Ia by choosing a probability threshold . We estimate the photometric redshifts ( photo-z ) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling . We obtain a mean bias ( \left < z _ { \mathrm { phot } } - z _ { \mathrm { spec } } \right > ) of 0.012 with \sigma \left ( \frac { z _ { \mathrm { phot } } - z _ { \mathrm { spec } } } { 1 + z _ { \mathrm { spec } } } % \right ) = 0.0294 without using a host-galaxy photo-z prior , and a mean bias ( \left < z _ { \mathrm { phot } } - z _ { \mathrm { spec } } \right > ) of 0.0017 with \sigma \left ( \frac { z _ { \mathrm { phot } } - z _ { \mathrm { spec } } } { 1 + z _ { \mathrm { spec } } } % \right ) = 0.0116 using a host-galaxy photo-z prior . Assuming a flat \Lambda CDM model with \Omega _ { m } = 0.3 , we obtain \Omega _ { m } of 0.298 \pm 0.008 ( statistical errors only ) , using the simulated LSST sample of photometric SNe Ia ( with intrinsic scatter \sigma _ { \mathrm { int } } = 0.07 ) derived using our methodology without using host-galaxy photo-z prior . Our method will help boost the power of SNe from the LSST as cosmological probes .