  The estimate of the remaining time of an ongoing wave of epidemic spreading
is a critical issue. Due to the variations of a wide range of parameters in an
epidemic, for simple models such as Susceptible-Infected-Removed (SIR) model,
it is difficult to estimate such a time scale. On the other hand,
multidimensional data with a large set attributes are precisely what one can
use in statistical learning algorithms to make predictions. Here we show, how
the predictability of the SIR model changes with various parameters using a
supervised learning algorithm. We then estimate the condition in which the
model gives the least error in predicting the duration of the first wave of the
COVID-19 pandemic in different states in India. Finally, we use the SIR model
with the above mentioned optimal conditions to generate a training data set and
use it in the supervised learning algorithm to estimate the end-time of the
ongoing second wave of the pandemic in different states in India.
