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

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

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.

This repository features a complete time-series forecasting project to predict the price movements of Bitcoin (BTC) against the US Dollar. The core of this project uses a Long Short-Term Memory (LSTM) neural network, a class of recurrent neural networks (RNNs) highly effective for sequence prediction tasks

Recurrent Neural Networks for Bitcoin price prediction

Recurrent Neural Networks (RNNs) for Bitcoin's price predictions

ested and evaluated different Deep Learning Models, 1.Recurrent Neural Network(RNN) and 2.Long Short-term Memory (LSTM) for Bitcoin Price Prediction. Compared the results of both model to see which is giving better prediction.

This project predicts Bitcoin prices using an LSTM (Long Short-Term Memory) model, a type of recurrent neural network specialized for time-series forecasting. It implements both single-step and multi-step predictions, forecasting BTC/USD prices up to 24 hours ahead. The model is trained on historical price data from Yahoo Finance.