  At present, cryptocurrencies have become a global phenomenon in financial
sectors as it is one of the most traded financial instruments worldwide.
Cryptocurrency is not only one of the most complicated and abstruse fields
among financial instruments, but it is also deemed as a perplexing problem in
finance due to its high volatility. This paper makes an attempt to apply
machine learning techniques on the index and constituents of cryptocurrency
with a goal to predict and forecast prices thereof. In particular, the purpose
of this paper is to predict and forecast the close (closing) price of the
cryptocurrency index 30 and nine constituents of cryptocurrencies using machine
learning algorithms and models so that, it becomes easier for people to trade
these currencies. We have used several machine learning techniques and
algorithms and compared the models with each other to get the best output. We
believe that our work will help reduce the challenges and difficulties faced by
people, who invest in cryptocurrencies. Moreover, the obtained results can play
a major role in cryptocurrency portfolio management and in observing the
fluctuations in the prices of constituents of cryptocurrency market. We have
also compared our approach with similar state of the art works from the
literature, where machine learning approaches are considered for predicting and
forecasting the prices of these currencies. In the sequel, we have found that
our best approach presents better and competitive results than the best works
from the literature thereby advancing the state of the art. Using such
prediction and forecasting methods, people can easily understand the trend and
it would be even easier for them to trade in a difficult and challenging
financial instrument like cryptocurrency.
