Predicting bitcoin prices: A machine learning approach for accurate forecasting

Abstract

This project investigates the active realm of Bitcoin price forecasting through the glass of machine intelligence models, including Logistic Regression, Support Vector Machines (SVM), and XGBoost Classifier. Leveraging a different dataset including historical and actual-occasion Bitcoin price dossier, the study employs an orderly method for dossier collection, feature collection, model preparation, and judgment. The aim is to embellish the veracity of short-term and unending forecasts, making the challenges posed apiece explosive cryptocurrency retail. The project extends further hypothetical exploration, climactic in the incident of a convenient web connect. This connects employs HTML, CSS, and Flask API to provide authentic-opportunity forecasts, extending the gap betwixt leading predictive models and proficient uses.