Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.

This project is for Bitcoin Price Prediction using Deep Learning Model LSTM.

LSTM-based Bitcoin price prediction using PyTorch. Explore deep learning for time-series forecasting with data preprocessing and model training. Ideal for cryptocurrency enthusiasts and those interested in financial forecasting.

Bitcoin price prediction with LSTM models using TensorFlow and Pandas. Train and evaluate models; visualize forecasts and compare predictions to actual prices 🐙

This is bitcoin price prediction model using various prediction models and reached good results. I develop this model for Deep Research in LSTM.

Hybrid Bitcoin price forecasting model combining Hidden Markov Models (HMM) for market regime detection and LSTM networks for next-day price prediction. Uses daily BTC data (2019–2024) and integrates hidden states with sequence learning to capture structural market shifts in financial time-series.

Predict Bitcoin prices with ML & DL models! 📈 Uses Ridge, Lasso, Random Forest, MLP, RNN & LSTM with hyperparameter tuning. 📊 Visualizes predictions & ROC curves. 🚀 Fetch data via yfinance, evaluate with MSE/R2. Perfect for crypto enthusiasts! 💸

Bitcoin Price prediction using an LSTM model

Bitcoin Price Prediction using Long Short-Term Memory Model (LSTM) and Hidden Markov Model (HMM)

LSTM-based hourly price prediction model for Bitcoin & Ethereum using technical analysis and deep learning.

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

The code loads a pre-trained LSTM model to predict Bitcoin prices. It fetches historical Bitcoin price data using the yFinance library. The data is normalized and split into training and testing sets. The LSTM model is used to predict future prices based on the test data. Predictions and actual prices are visualized in Streamlit.

A Streamlit-based web app for 14-day Bitcoin price prediction using LSTM+CNN and XGBoost models. The app visualizes real BTC prices, ML-generated forecasts, and a moving average baseline. Automated daily updates with GitHub Actions.

Bitcoin price prediction model using LSTM and RNN involving Technical Indicators.

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Bitcoin Price Prediction using ARIMA Model and LSTM network

Creating a price prediction system using LSTM, GRU and a stacked model on trained on Bitcoin, Ethereum and Ripple price data

Bitcoin price prediction using Long Short-Term Memory (LSTM) models is a popular application of deep learning in time series forecasting. Bitcoin’s price is highly volatile and influenced by various factors such as market demand, regulatory news, technological advancements, and investor sentiment. .

Bit Predict is a bitcoin price predictor which predicts the price of bitcoin using previous timeseries data (Timeseries Forecasting). Evaluated the results of a series of deep learning model such as sequence models (LSTM,1D CNN), ensemble model, dense model, multivariate model, also replicated the N-BEATS algorithm using TensorFlow layer subclassing and made prediction with the best performing model.

This project is a Flask-based web application for predicting Bitcoin (BTC) prices. The application fetches historical BTC price data, trains a machine learning model using LSTM neural networks, and provides predictions for the next 7 days.

This project analyzes 4 years of Bitcoin (BTC-USD) historical data, visualizes trends using moving averages, and predicts future prices with an LSTM model. It also integrates the Fear & Greed Index for multi-feature cryptocurrency prediction, enhancing model accuracy and insights.

This project demonstrates a deep learning approach to forecast Bitcoin prices using an LSTM (Long Short-Term Memory) model, followed by a backtesting strategy to evaluate the effectiveness of the predictions in a simulated trading environment.

Probabilistic Bitcoin (BTC) price forecasting using an LSTM meta-model. An NSGA-II algorithm performs 3-objective hyperparameter optimization, balancing prediction error, complexity, and training time. The final output is not a single price point but a probabilistic range (quantile regression), providing a richer, more risk-aware forecast.

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Basic Bitcoin Price Prediction Model

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Bitcoin Price Prediction Using LSTM Model

Bitcoin Price Prediction using LSTM Model.

Bitcoin price prediction analysis using LSTM models.