• Hitesh Ghadage

In the past eight years of Bitcoin’s history, the economy has seen the price of Bitcoin rapidly grow due to its promising outlook on the future for crypto currencies. Investors have taken note of several advantages Bitcoin provides over the traditional banking system. One such trait is that Bitcoin allows for decentralized banking, meaning that Bitcoin cannot be regulated by powerful banks. There is also a market cap of 21 million Bitcoins that can be in circulation, therefore a surplus of Bitcoins cannot be “printed” which would result in inflation. Bitcoin resolves the issue with transaction security by using a block chain, or a ledger, which records the history of every transaction ever made into one long hexadecimal “chain” of anonymous transactions, which keeps transaction history transparent, but also confidential. Bitcoin as a result has become a very bullish investing opportunity, and due to the huge volatility of the Bitcoin market price, this paper attempts to aid in investment decision making by providing Bitcoin market price prediction. Our team explored several Machine Learning algorithms by using Supervised Learning to train a prediction model and provide informative analysis of future market prices. We start with Linear Regression models, and train on several important features. We show that the price of Bitcoin can be predicted with Machine Learning with high degree of accuracy.

Bitcoins, Crypto Currency, Deep Learning, Machine Learning

"BEST PRACTICE TO MONITORING THE CRYPTOCURRENCY USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.c8-c13, August-2021, Available :http://www.jetir.org/papers/JETIR2108252.pdf

"BEST PRACTICE TO MONITORING THE CRYPTOCURRENCY USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppc8-c13, August-2021, Available at : http://www.jetir.org/papers/JETIR2108252.pdf