Analysis of Bitcoin Price using ML Algorithm

After the recent ups and downs in cryptocurrency values, bitcoin is now more frequently seen as a valuable investment. Due to its extreme volatility, there is a stronger requirement for precise forecasts that support investment decisions. Although many studies have employed machine learning to provide more precise predictions of the Bitcoin price, very few studies have concentrated on the viability of using different modeling techniques to sample the data structures and different features. We can sort the price of bitcoin into two main classes such as the daily price and the high-frequency price, then use ML algorithms to anticipate the cost at various frequencies. The fundamental trade highlights obtained from a digital money trade are used for 5-minute span cost expectation, while a collection of high- dimension highlights including property and organization, exchanging and market, consideration, and gold spot cost are used for daily price prediction. At the everyday cost forecast of Bitcoin with high-layered data, measurable methods like strategic relapse and direct discriminant examination are utilized, beating more perplexing AI calculations. AI models, for example, Irregular Timberland, XGBoost, Quadratic Discriminant Examination, Backing Vector Machine, and Long Transient Memory are seen to beat measurable strategies for the 5-minute stretch value expectation of Bitcoin