Cryptocurrency Market Price Prediction using Data Science and Machine Learning Techniques

This research study focuses on the application of regression algorithms and other machine learning approaches in predicting future values of the bitcoin market. These algorithms are able to determine the underlying correlations and patterns that cause price changes by examining past price data in conjunction with important influencing elements like trade volume, market sentiment, and GDP. This research sets a benchmark by assessing the effectiveness of less complicated methods using linear regression, which is a fundamental technique in predictive modeling. The results provide a basis for understanding market dynamics by demonstrating how linear regression can capture simple trends. The study does point out that more advanced regression techniques, such as polynomial and support vector regression, need to be explored to enhance the accuracy of the forecast because bitcoin trading is inherently complex. The purpose of this study is to compare several regression approaches, hoping to show where they excel and where they fall short. Its ultimate goal is to help build better prediction models for the bitcoin industry. For stakeholders and investors trying to make sense of the complex world of digital currencies, this study does more than just shed light on market behavior; it also offers practical consequences.