A Data Selection Methodology to Train Linear Regression Model to Predict Bitcoin Price

Abstract

Bitcoin is the most popular and valuable cryptocurrency in the financial market which attracts traders for investment and opens new research opportunities for researchers. Many research works have been done on bitcoin price prediction with different machine learning prediction algorithms. Researchers take relevant features from the dataset which have strong correalation with bitcoin prices and select random data chunks to train and test their model. Randomly selected data to train the model, may cause inappropriate results and reduce the accuracy of price prediction. In this paper, we investigate a proper data selection method to train a prediction model. We apply our proposed methodology to train a simple linear regression prediction algorithm. We predict bitcoin price for 7 days with the linear regression model. When we train the linear regression model with an appropriate data chunk identified by our methodologies, we find acceptable results for the prediction. The percentage error method is applied for error calculation which finds the accuracy is 96.97%. In the end of this manuscript, we conclude our work with future improvements.