Random Forest Analysis for Key Factors in Bitcoin Price Prediction
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This research explores the application of the Random Forest algorithm to predict Bitcoin price fluctuations. Given Bitcoin's high volatility and the influence of various factors such as market sentiment, macroeconomic variables, and blockchain-specific metrics, Random Forest was chosen for its capability to handle complex and non-linear relationships. The dataset includes trading volume, market capitalization, mining difficulty, and social media sentiment indicators. Data preprocessing techniques such as normalization, handling missing values, and adding temporal features were employed to enhance prediction quality. Model evaluation using Mean Absolute Error (MAE = 0.15), Mean Squared Error (MSE = 0.25), and R-squared (R² = 0.85) demonstrates the model's robust performance in capturing intricate market dynamics. The study highlights the importance of feature importance rankings in identifying key drivers of Bitcoin price movements, offering valuable insights for traders, regulators, and investors. Despite its success, areas for improvement include incorporating additional features, such as real-time sentiment analysis and advanced time-series predictors, to further enhance predictive accuracy and applicability across volatile market conditions.
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