Bitcoin Price Forecasting Using Data Mining Techniques

Abstract

This research aims to Study and develop a Bitcoin price forecasting model using data mining techniques including Linear Regression, Moving Averages, Logistic Regression and Exponential Smoothing. Using Bitcoin price data from January 1, 2021 to December 31, 2021, the data has 6 attributes: Day/Month/Year, Open Price, Highest Price of the Day, Price. the day's low, the day's trading volume and the percentage change from the previous day's closing price. The results showed that Exponential smoothing techniques is most suitable The model performance for the Train dataset yielded Root Mean Square Error : RMSE of 0.10047164 and Mean Absolute Error : MAE of 0.07726155. For the Test data set, the square root of the mean squared error was 0.05091178 and the mean absolute deviation was 0.04143874. moving average Logistics regression analysis and exponential smoothing