Predicting Bitcoin Price Direction Using Machine Learning Models

Tanju Açi; Hakan Kekül

Makale detayları

Predicting Bitcoin Price Direction Using Machine Learning Models

Yazarlar:

Tanju Açi | Hakan Kekül

Yayıncı:

Sivas Cumhuriyet University

Ana Başlıklar:

Veri Mühendisliği ve Veri Bilimi | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi Vol.2 Issue 2, pp.205-213

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332590

pISSN:

3023-7203

Anahtar kelimeler:

Özet:

 In the financial sector, as past economic and social events have shaken trust, this trust is being regained through the internet and computer technologies. Emerging in the 19th century, financial technology has led to a new economic understanding with digital money and especially bitcoin. The decentralized structure of bitcoin and the encryption systems used for security play an important role in preventing fraud and have become the center of attention of investors. As its value has increased, studies on price predictions have naturally increased. This study aims to predict the impact of data obtained from digital economy news sites on bitcoin price using natural language processing and machine learning techniques. In line with this goal, text vectorization was performed with the TF-IDF statistical method. Synthetic Minority Oversampling Technique SMOTE was applied to eliminate the imbalance in the vectorized data set. Classification models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbor, Extra Trees, Bernoulli Naive Bayes and Multilayer Perceptron were applied to the obtained output. According to the results of the performance of different machine learning models in predicting the direction of bitcoin price fluctuation, the Extra Trees Classifier model showed the highest performance with an Accuracy of 86.71, recall of 86.71, precision of 86.99 and F1 score of 86.59.

 In the financial sector, as past economic and social events have shaken trust, this trust is being regained through the internet and computer technologies. Emerging in the 19th century, financial technology has led to a new economic understanding with digital money and especially bitcoin. The decentralized structure of bitcoin and the encryption systems used for security play an important role in preventing fraud and have become the center of attention of investors. As its value has increased, studies on price predictions have naturally increased. This study aims to predict the impact of data obtained from digital economy news sites on bitcoin price using natural language processing and machine learning techniques. In line with this goal, text vectorization was performed with the TF-IDF statistical method. Synthetic Minority Oversampling Technique SMOTE was applied to eliminate the imbalance in the vectorized data set. Classification models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbor, Extra Trees, Bernoulli Naive Bayes and Multilayer Perceptron were applied to the obtained output. According to the results of the performance of different machine learning models in predicting the direction of bitcoin price fluctuation, the Extra Trees Classifier model showed the highest performance with an Accuracy of 86.71, recall of 86.71, precision of 86.99 and F1 score of 86.59.

 In the financial sector, as past economic and social events have shaken trust, this trust is being regained through the internet and computer technologies. Emerging in the 19th century, financial technology has led to a new economic understanding with digital money and especially bitcoin. The decentralized structure of bitcoin and the encryption systems used for security play an important role in preventing fraud and have become the center of attention of investors. As its value has increased, studies on price predictions have naturally increased. This study aims to predict the impact of data obtained from digital economy news sites on bitcoin price using natural language processing and machine learning techniques. In line with this goal, text vectorization was performed with the TF-IDF statistical method. Synthetic Minority Oversampling Technique SMOTE was applied to eliminate the imbalance in the vectorized data set. Classification models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbor, Extra Trees, Bernoulli Naive Bayes and Multilayer Perceptron were applied to the obtained output. According to the results of the performance of different machine learning models in predicting the direction of bitcoin price fluctuation, the Extra Trees Classifier model showed the highest performance with an Accuracy of 86.71, recall of 86.71, precision of 86.99 and F1 score of 86.59.

 Finans sektöründe, geçmişte yaşanan ekonomik ve sosyal olaylar güveni sarsarken, internet ve bilgisayar teknolojileri sayesinde bu güven yeniden kazanılıyor. 19. yüzyılda ortaya çıkan finansal teknoloji, dijital para ve özellikle bitcoin ile yeni bir ekonomik anlayışa yol açmıştır. Bitcoinin merkezi olmayan yapısı ve güvenlik için kullanılan şifreleme sistemleri dolandırıcılığın önlenmesinde önemli rol oynamış ve yatırımcıların ilgi odağı haline gelmiştir. Değeri arttıkça fiyat tahminleri üzerine yapılan çalışmalar da doğal olarak artmıştır. Bu çalışma, dijital ekonomi haber sitelerinden elde edilen verilerin bitcoin fiyatı üzerindeki etkisini doğal dil işleme ve makine öğrenmesi tekniklerini kullanarak tahmin etmeyi amaçlamaktadır. Bu hedef doğrultusunda TF-IDF istatistiksel yöntemi ile metin vektörleştirmesi yapılmıştır. Vektörleştirilen veri setindeki dengesizliği gidermek için Sentetik Azınlık Aşırı Örnekleme Tekniği SMOTE uygulandı. Elde edilen çıktılara Lojistik Regresyon, Karar Ağaçları, Rastgele Orman, Destek Vektör Makineleri, K-En Yakın Komşu, Ekstra Ağaçlar, Bernoulli Naive Bayes ve Çok Katmanlı Perceptron gibi sınıflandırma modelleri uygulanmıştır. Farklı makine öğrenimi modellerinin bitcoin fiyat dalgalanmasının yönünü tahmin etmedeki performans sonuçlarına göre, Ekstra Ağaçlar Sınıflandırıcı modeli 86,71 Doğruluk, 86,71 hatırlama, 86,99 kesinlik ve 86,59 F1 puanı ile en yüksek performansı göstermiştir.

Makale detayları

Predicting Bitcoin Price Direction Using Machine Learning Models

Yazarlar:

Tanju Açi | Hakan Kekül

Yayıncı:

Sivas Cumhuriyet University

Ana Başlıklar:

Veri Mühendisliği ve Veri Bilimi | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi Vol.2 Issue 2, pp.205-213

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332590

pISSN:

3023-7203

Artificial Intelligence

Bitcoin

Machine Learning

Natural Language Processing

Stock Market

CryptoYapay Zela

Bitcoin

Makine Öğrenmesi

Doğal Dil İşleme

Borsa

Kripto

Sayı

Makale detayları

Yazarlar: Tanju Açi | Hakan Kekül

Yayıncı: Sivas Cumhuriyet University

Ana Başlıklar: Veri Mühendisliği ve Veri Bilimi | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi: Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi Vol.2 Issue 2, pp.205-213

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001332590

pISSN: 3023-7203

Sayı

Yazarlar:

Tanju Açi | Hakan Kekül

Yayıncı:

Sivas Cumhuriyet University

Ana Başlıklar:

Veri Mühendisliği ve Veri Bilimi | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi Vol.2 Issue 2, pp.205-213

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332590

pISSN:

3023-7203