Revealing the Tendency of Analytical Business Enterprises Toward Currency and Prices Via the Naive Bayes Algorithm

Abstract

Machine learning is a cornerstone of data science, enabling the analysis and prediction of complex data patterns. Among various algorithms, Naive Bayes is a popular probabilistic classifier based on Bayes' theorem, assuming strong independence among features. Its efficiency in handling large datasets, coupled with ease of implementation, makes it a valuable tool in data science workflows. The purpose of this study is to illustrate the research and development trends of Turkish business enterprises based on unit of measure (national currency or US dollars) and price basis measurements by the Naive Bayes algorithm. The result of classifying shows that economic activities show varied preferences for currency and price bases: agriculture, forestry, and fishing, construction, and sewerage, waste management, and remediation activities are split equally between national and US dollar currencies and price types. Manufacturing favors current prices; mining and quarrying prefer national currency and current prices, while services lean towards the US dollar and current prices. Business enterprises with all economic activities prioritize the national currency (67%) and constant prices (67%).

Keywords

• Machine learning
• Naive Bayes
• analytical business enterprise
• currency
• price

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