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This study analyzes and visualizes Bitcoin prices and trends using a variety of approaches, ranging from library imports to prediction modeling. It starts with the pre-processing of data and then proceeds to use machine learning algorithms to predict Bitcoin prices. The paper finishes by suggesting a reliable prediction method that uses an ensemble approach to combine gradient boosting and random forest regressors. Comparative examination indicates the ensemble model’s greater forecasting power in anticipating Bitcoin price movements. It lastly establishes the best ensemble method to be voting.

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An Ensemble Machine Learning-Based Approach Toward Accuracy in Bitcoin Price Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Sumeda Puja,
  • Veeravalli Ruthvik,
  • Varun Dhanashree,
  • K. S. Saee Ganesh,
  • C. Deepti

摘要

This study analyzes and visualizes Bitcoin prices and trends using a variety of approaches, ranging from library imports to prediction modeling. It starts with the pre-processing of data and then proceeds to use machine learning algorithms to predict Bitcoin prices. The paper finishes by suggesting a reliable prediction method that uses an ensemble approach to combine gradient boosting and random forest regressors. Comparative examination indicates the ensemble model’s greater forecasting power in anticipating Bitcoin price movements. It lastly establishes the best ensemble method to be voting.