Bitcoin Price Prediction

Accurately forecasting bitcoin values is a significant difficulty in financial time-series analysis. Inorder to estimate the closing price of Bitcoin, this study presents a deep learning method based on LongShort-Term Memory (LSTM) neural networks. To optimize model performance, the ten-year historical datafrom Yahoo Finance was preprocessed using sequence generation and normalization. The Kerasimplemented LSTM model, which provides dynamic graphics and user-defined forecasting capabilities, isdeployed via a web application developed in Streamlit. The model does a good job of detecting temporaltrends and reliably generating short-term projections. By contrasting the expected and actual numbers, themodel's effectiveness is validated. In addition to giving investors practical tools, this initiative promotes thescience of data-driven financial forecasting. The technology also makes it easier for non-technicalindividuals to use by facilitating prediction and real-time involvement. Future studies may focus onintegrating sentiment analysis with hybrid models to improve long-term forecast accuracy.