bitcoin_prediction

Continuously fetches Bitcoin price and Twitter sentiment using Python scripts, storing data in CSV files for up-to-date predictions, as outlined in the data gathering section.

Uses LSTM neural networks that outperformed ARIMA models in experiments, providing better accuracy for time-series forecasting, as mentioned in the core engine description.

Incorporates Twitter sentiment analysis as a feature for price prediction, enhancing accuracy by combining market data with social media trends.

Generates buy/sell decisions based on predicted prices and configurable thresholds, making it actionable for trading applications, with output stored in a MySQL database.

Offers a full pipeline from data collection to visualization via Tableau, emphasizing practical use over theoretical exploration, as highlighted in the philosophy.