Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning

In the wake of recent pandemic COVID-19, we explore its unprecedented impact on demand and supply cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory Noise (NBG). The study employed filters to enhance performance named NBG. Dataset utilized for this analysis were obtained from website Coin Market Cap, including: Binance (BCN), BitCoin Cash (BCH), (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), daily data collected exchange markets platforms spans 2nd January 2018 7th July 2020. Auto encoder was labelling trading strategies buy-hold-sell.