ajay35 / cryptocurrency-price-prediction

Predictive Analysis of Cryptocurrency Price Using Deep Learning

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN), Gated Recurrent Unit (GRU), Convolution1D and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The pro- posed approach is implemented in Python and validated for benchmark datasets.

Files

Data Ananlysis

1. btcoin_Correlation.ipynb
2. btcoin_Correlation_analysis.ipynb

Neural network models

1. bitcoinlarge.ipynb - This file is the paper implementaion of LSTM on USD large bitcoin dataset of kaggle
2. bitcoinsmall.ipynb - This file is the paper implementaion of LSTM on USD small bitcoin dataset of kaggle
3. compare_LSTM_GRU_CONV1D.ipynb - Comparison of LSTM, GRU and CONV1D on bitcoin dataset
4. comparewithtimesteplargew_l.ipynb - Comparison of error with change in timestep on 25000000 data
5. comparewithtimestepsmallw_l.ipynb - Comparison of error with change in timestep on 12000000 data
6. comparewithvaryhiddenstep.ipynb - Comparison of error with change in hidden units of LSTM
7. performance_on_various_crytocurrency.ipynb - Performance of LSTM on price prediction of various crytocurrencies
8. tweet_bitcoin.ipynb - Performce of bitcoin price prediction on incorporating twitter sentiment regarding bitcoin

Data

This folder contains various crytocurrency dataset extracted from coinmarket

Model

Various model that was built and saved for bitcoin prediction

Bitcoin dataset link

Bitcoin dataset