Cryptocurrency Price Forecasting with Sentiment-Driven Alerts using ML

Cryptocurrency price forecasting is a challenging task due to market volatility and unpredictable investor behavior. This project introduces a sentiment-driven machine learning approach to improve prediction accuracy. Sentiment data is collected from platforms like Twitter and financial news using Natural Language Processing (NLP) techniques. These sentiments are quantified and combined with historical price data to form a robust dataset. An LSTM (Long Short-Term Memory) model is used for its ability to learn temporal dependencies in timeseries data. The model is trained to forecast future price movements by understanding both technical trends and public sentiment. Alerts are generated based on significant sentiment-influenced price changes to assist users in decisionmaking. The system utilizes Python, TensorFlow, and relevant APIs for data extraction and preprocessing. React is used for building the front-end interface, allowing interactive user experiences. The approach bridges the gap between numerical analysis and behavioral finance. Evaluation metrics such as RMSE and MAE are used to assess the model’s performance. Results show that incorporating sentiment data improves forecast accuracy. The application is scalable and adaptable for real-time use. Future enhancements include live data integration and predictive analytics for trade suggestions. This project highlights the power of combining sentiment analysis with deep learning in financial forecasting..