• YAPATLA RAVI
• VENKAT RAM REDDY G

Over the last few years the interest in machine learning and AI-assisted trade have been increasing. We use this approach here to test the hypothesis that to produce abnormal income, the inefficiency of the crypto-currency market can be exploited. We review daily data for the period between Nov. 2015 and Apr. 2018 for 1, 681 crypto currencies. We prove that basic market methods, enabled by sophisticated machine learning algorithms, surpass traditional requirements. The findings suggest that algorithmic processes that are not trivial but essentially easy will help predict the growth of the crypto currency industry in the foreseeable future. In 2017, the interest of crypto currencies increased because of the mega- exponential rise in market capitalization for several consecutive months. There are currently more than 1,500 publicly trading crypto currencies capitalizing over $300 billion, with a market capitalization record in January 2018 totaling more than $800 billion. According to a recent study, between 2.9 million and 5.8 million private and institutional investors are working in the various transaction networks. In a variety of online markets, big crypto currencies can be acquired using fiat currency and then used to buy less common crypto currencies in turn. The regular trading amount is reportedly in excess of $15 billion. More than 170 hedge funds investing in crypto currencies have appeared since 2017 and bitcoin futures were released to meet investor appetite for Bitcoin trading and hedging.

Crypto Currency, Bitcoin , Machine Learning

"BIT COIN PREDICTION USING RECURRENT NEURAL NETWORK ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.114-118, October-2020, Available :http://www.jetir.org/papers/JETIR2010012.pdf

"BIT COIN PREDICTION USING RECURRENT NEURAL NETWORK ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp114-118, October-2020, Available at : http://www.jetir.org/papers/JETIR2010012.pdf