Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the MNIST dataset, namely, a CNN using Keras and a fully-connected network using TensorFlow.

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.

Recurrent Neural Network (RNN), LSTM (Long Short-Time Memory), Bitcoin, Google Trends, Prediction, Deep Learning

Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction

LSTM neural network predicting price movements of Bitcoin, backtesting and visualisations.

This project uses a Long Short-Term Memory (LSTM) model, a type of recurrent neural network, to predict the future price of Bitcoin.

This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

In this project, I created prediction model for predict bitcoin price with Gated Recurrent Unit Model. GRU is a gating mechanism in recurrent neural networks (RNN) similar to a long short-term memory (LSTM), GRU have more simple computation and faster than LSTM because have fewer number of gates.

Bitcoin trading bot using an LSTM neural network in Keras

[Python] Adopted Neural Network (LSTM), Time Series (ARIMA) and Sentiment Analysis (NLP) to predict Bitcoin prices

Using NLP and LSTM Neural Network to predict Bitcoin Prices

Timeseries forecast on Bitcoin stock price with ARIMA, Prophet, LSTM Recurrent Neural Network and XGBoost.

An exploration into Recurrent Neural Networks using LSTM. The implmentation is named Cryptonet as it attempts to model trends in the price of bitcoin of varying lengths. Results of the experiment can be found within the results folder.

LSTM Neural Network for predicting future price itself

Building a Recurrent Neural Network with Long Short Term Memory (LSTM) layers to predict the price of Bitcoin that yields prediction results

This project uses a Long Short-Term Memory (LSTM) neural network to predict the price of Bitcoin in US dollars. The LSTM model is implemented using the Keras library in Python.

Using autoencoder and LSTM neural networks to identify anomalies in bitcoin trading

I wrote all the code and every section besides some of the intro.

A short python script that use Recurrent Neural Network and LSTM to predict next period Bitcoin price from 30 previsous periods

This python script predicts the price of bitcoin using LSTM neural network

A very simple predictor for bitcoin prices using a LSTM neural network

An algorithm to predict bitcoin prices build by utilising chaos theory and neural networks (MLP,LSTM)

🚀 Bitcoin price prediction using RNN & LSTM neural networks with 30-day forecasting. Complete ML pipeline with data extraction, preprocessing, and time series analysis.

Crypto Price Prediction with LSTM uses machine learning and LSTM neural networks to forecast Bitcoin (BTC-USD) prices based on historical data. This project is for educational purposes only and should not be used for actual financial decisions due to cryptocurrency market volatility.

This repository features a Bitcoin (BTC) Trading Bot powered by a Long Short-Term Memory (LSTM) neural network. The project focuses on leveraging predictive analytics to automate trading strategies and assess performance through historical price data.

This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

This project leverages a Long Short-Term Memory (LSTM) neural network to predict Bitcoin prices based on historical data. The model is trained and evaluated in a Jupyter Notebook, and an interactive interface is provided via a Streamlit application.