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Performance Evaluation and Analysis of Supervised Machine
Learning Algorithms for Bitcoin Cryptocurrency Price Forecast
Sani Abba, Souley Boukhari, Mohammed Ajuji , Amina Nuhu Muhammad
Pages - 28 - 42 | Revised - 30-06-2022 | Published - 01-08-2022
MORE INFORMATION
KEYWORDS
Bitcoin, Cryptocurrency, Machine-Learning, Prediction.
ABSTRACT
Earlier to the advent of computers and the internet. Transactions such as buying, selling, hiring,
and cash transfer are performed physically, hand-to-hand and/or face-to-face using hard-printed
currency also known as traditional means. The recent advances in internet and networking
technologies have significantly refurbished and improved the methods and limitations of the
traditional ways, through cryptocurrency or digital money especially in terms of cost, speed, and
access. These technologies which bring people together irrespective of geographical location have
fashioned a revolution in trading and transaction processing; online transaction processing and
real-time processing. However, like every other pioneering development, this is not without
resistance from stakeholders, whom have been using the traditional means for long; its validity and
legitimacy have been seriously challenged. In this work, several models leveraged to forecast
bitcoin price were Linear Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine
(SVM), Decision Tree (DT), Elastic Net Regression (EN), Lasso Regression (Lasso) and Ridge
regression (RR). The models’ accuracies were determined and evaluated using Mean Absolute
Error (MAE), Mean Square Error (MSE), and R Square Error (R2). It revealed good performance
except for SVM which falls in the negative even after fine-tuning and improved performance. The
LR led in performance, then EN, Lasso, and RR. Decision Tree on the other hand present an
encouraging and challenging result. Whereas the SVM model presents worst-case prediction
accuracy of -22.38%. Therefore, the linear regression model has the best fit for bitcoin price
prediction amongst the algorithms tested and evaluated. The study would reduce researchers
throughput by presenting firsthand model for price prediction, support vector machine need to be
further studied to unravel reasons for its undesirable performance.
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Dr. Sani Abba
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi - Nigeria
Professor Souley Boukhari
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi - Nigeria
Mr. Mohammed Ajuji
Department of Computer Science, Gombe State University, Gombe - Nigeria
majuji@gsu.edu.ng
Mrs. Amina Nuhu Muhammad
Department of Computer Science, Gombe State University, Gombe - Nigeria
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