BIT COIN PRICE PREDICTION USING RF ALGORITHM WITH SENTIMENT FACTORS

Sentiment analysis was included into a predictive model that was created for this project in order to forecast bitcoin closing values. The dataset contains daily Bitcoin data for January 2024, including sentiment scores computed for each day as well as opening, high, low, and closing values. The study assesses the predictive ability of the Random Forest regression model by using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R^2) score. The negative R^2 score indicates that, although the model may produce predictions, its overall fit to the data is still not ideal, implying that the model is unable to adequately represent the correlation between sentiment ratings and Bitcoin prices. Potential areas for development to increase the predicted accuracy of the model and gain a deeper understanding of how sentiment analysis affects Bitcoin prices include feature engineering, hyper parameter tuning, and model selection.