  Large scale solar eruptions significantly impact space weather and damages
space-based human infrastructures. It is necessary to predict large scale solar
eruptions, which will enable us to protect our vulnerable infrastructures of
modern society. We aim to investigate the difference between flaring and
non-flaring active regions. We use photospheric vector magnetogram data from
Solar Dynamic Observatory's Helioseismic Magnetic Imager to study the time
evolution of photospheric magnetic parameters on the solar surface. We build a
database of flaring and non-flaring active region observed on the solar surface
from the years 2010 to 2017. We train the machine learning algorithm by the
time evolution of these active region parameters. Finally, we estimate the
performance obtained from this machine learning algorithm. We find the strength
of some magnetic parameters namely total unsigned magnetic flux, total unsigned
magnetic helicity, total unsigned vertical current and total photospheric
magnetic energy density in flaring active regions are much higher compared to
the non-flaring ones. These magnetic parameters in the flaring active region
are highly evolving and complex. We are able to obtain good forecasting
capability with a relatively high value of true skill statistic (TSS). We also
find that time evolution of total unsigned magnetic helicity and total unsigned
magnetic flux have very high ability to distinguish flaring and non-flaring
active regions. It is possible to distinguish flaring active region from the
non-flaring one with good accuracy. We confirm that there is no single common
parameter which can distinguish all flaring active regions from the non flaring
one. However, time evolution of top few magnetic parameters namely total
unsigned magnetic flux and total unsigned magnetic helicity have very high
distinguishing capability.
