- Saturday, Jan 3rd, 2026
| TITLE | Real-Time Bitcoin Price Prediction using XG-Boost and LSTM Model |
|---|---|
| ABSTRACT | The necessity for precise and timely price forecasting has grown due to the swift expansion of cryptocurrency marketplaces, particularly for extremely volatile commodities like Bitcoin. This project introduces a real-time A system for forecasting The worth of bitcoin that combines LSTM, or extended short-term memory and XGBoost models. To improve data quality, both historical in addition to current Data on bitcoin prices are gathered and preprocessed using noise reduction and normalization techniques. While LSTM is used to learn long-term sequential relationships within time-series data, XGBoost is used to find intricate nonlinear patterns and short-term price changes. Prediction robustness and reliability are increased by integrating these models. The Streamlit framework is used to deploy the system, which offers an interactive web-based interface that shows real-time prices, anticipated trends, and analytical insights. Experimental. Based on experimental data, The recommended approach provides a useful tool for bitcoin price analysis and decision support and successfully captures market behavior. |
| AUTHOR | Archana H R, Naseerhusen Ankalagi PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India |
| VOLUME | 12 |
| DOI | DOI:10.15680/IJARETY.2025.1206025 |
| 25_Real-Time Bitcoin Price Prediction using XG-Boost and LSTM Model.pdf | |
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