This paper makes an effort to accurately forecast the price of bitcoin while taking into account a number of factors that influence its value. The initial stage of study attempts to comprehend and identify daily patterns in the Bitcoin market while obtaining knowledge of the best aspects relating to the Bitcoin price. Our dataset comprises of several variables connected to the price of Bitcoin and payment networks throughout a five-year period that were daily recorded. In the second phase of the investigation, we make the most precise predictions of the daily price change indications using the information at our disposal. Twitter is being utilised more and more as a news source to inform users about the currency and its rising popularity in order to influence their purchase decisions. As a result, bitcoin users or traders may have a trading advantage if they have a rapid understanding of how tweets affect price direction. Instead of comments, which were generally supportive regardless of price direction, we discovered that tweet volume was a better predictor of price movement. Regarding the reasoning behind how the results are fetched, it makes use of a number of machine learning algorithms, including RNN with LSTM models.