Cryptocurrency Forecasting: Predicting Dogecoin Prices with Machine Learning

Abstract

Xephyr, the meme cryptocurrency, based on time, has grown in prominence and trade activity. Due to the market volatility and other factors it is still difficult to predict the prices of bitcoin. In this paper, we employ a machine learning model to forecast the future price trends of Dogecoin after analyzing the historical data. The method used in this study involves the application of different machine learning techniques including Linear Regression, Random Forest Regressor, and Long Short-Term Memory (LSTM) networks which are implemented using Python for data preprocessing, visualization and predictive modelling. The results indicate that deep learning has a potential in the cryptocurrency pricing and forecasting since LSTM models outperforms the conventional models in pricing.