  Cryptocurrencies have become a popular and widely researched topic of
interest in recent years for investors and scholars. In order to make informed
investment decisions, it is essential to comprehend the factors that impact
cryptocurrency prices and to identify risky cryptocurrencies. This paper
focuses on analyzing historical data and using artificial intelligence
algorithms on on-chain parameters to identify the factors affecting a
cryptocurrency's price and to find risky cryptocurrencies. We conducted an
analysis of historical cryptocurrencies' on-chain data and measured the
correlation between the price and other parameters. In addition, we used
clustering and classification in order to get a better understanding of a
cryptocurrency and classify it as risky or not. The analysis revealed that a
significant proportion of cryptocurrencies (39%) disappeared from the market,
while only a small fraction (10%) survived for more than 1000 days. Our
analysis revealed a significant negative correlation between cryptocurrency
price and maximum and total supply, as well as a weak positive correlation
between price and 24-hour trading volume. Moreover, we clustered
cryptocurrencies into five distinct groups using their on-chain parameters,
which provides investors with a more comprehensive understanding of a
cryptocurrency when compared to those clustered with it. Finally, by
implementing multiple classifiers to predict whether a cryptocurrency is risky
or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.
