Bitcoin Price Prediction Using LSTM-Based Deep Learning Techniques in Python: A Data-Driven Approach

This study examines the cryptocurrency markets that have experienced exponential growth over the past decade, with Bitcoin emerging as the most dominant and widely traded digital currency. However, the inherent volatility and non-linear behavior of Bitcoin prices pose significant challenges in forecasting. This study aims to investigate the efficacy of deep learning models, particularly Long Short-Term Memory (LSTM) networks, in predicting Bitcoin prices using historical data. The research utilizes daily price data, cleansed and normalized, and applies LSTM-based neural networks to capture the temporal dynamics of Bitcoin’s market behavior. The model was trained using Python-based frameworks such as Keras and TensorFlow, and its performance was evaluated using standard error metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results indicate that the LSTM model is capable of accurately predicting future trends in Bitcoin prices, outperforming traditional linear forecasting methods. This research highlights the growing relevance of deep learning in the financial sector, especially in volatile and data-rich environments like cryptocurrency. Keywords: Bitcoin; Deep Learning; LSTM; Price Prediction; Time-Series Forecasting; Python; Cryptocurrency