Veri Madenciliği ve Makine Öğrenimi Yaklaşımlarının Karşılaştırılması: Tekstil Sektöründe bir Uygulama

Öz

Her gün gelişmekte ve büyümekte olan teknoloji, modern dünyanın vazgeçilmez bir unsuru olmuştur. Teknolojinin hızla gelişmesiyle bilgisayar kullanımı artan dünyamızda daha fazla veri depolanmaya başlanmıştır. Oluşan bu büyük veriler tek başlarına bir anlam ifade etmemektedir. Ancak veri ve analitik alanda yetkinliklerin artırılması ile belirli örüntülere dayalı çıkarımlardan anlamlılık boyutu kazanırlar. Örüntülerin belirlenebilmesini sağlayan, yapılacak araştırmaya ve veri tipine uygun veri madenciliği ve makine öğrenimi teknikleri bulunmaktadır. Bu teknikleri ile veriler arasındaki kural, kalıp ve ilişkiler bulunur. Veri madenciliği ve makine öğrenimi teknikleri birçok farklı sektörde farklı amaçlarla kullanılabilmektedir. Bu çalışmada veri madenciliği ve makine öğrenimi arasındaki benzerlik ve farklılıklar ortaya konmaya çalışılmış ve bu disiplinlerin; veri bilimi, istatistik ve diğer disiplinler ile ortak ve ayrıştığı noktalar tespit edilmeye çalışılmıştır. Ayrıca çalışmada pantolon üreten bir tekstil firmasının verileri kullanılarak, R Studio, Python ve Knime makine öğrenimi programları yardımıyla, çoklu doğrusal regresyon, yapay sinir ağları ve karar ağaçları teknikleri uygulanmış, tahmini model sonuçlar bulunmuş ve model performansları karşılaştırılmıştır. Çalışmanın sonucunda tahminleme başarısında en iyi algoritmanın yapay sinir ağları ve en iyi makine öğrenimi programının RStudio programı olduğu sonucuna varılmıştır.

Anahtar Kelimeler

• Tekstil Sektörü
• Makine Öğrenimi
• Çoklu Doğrusal Regresyon
• Yapay Sinir Ağları
• Karar Ağaçları
• Python
• RStudio
• Knime

Comparison of Data Mining and Machine Learning Approaches: An Application in Textile Industry

Abstract

Technology, which is developing and growing every day, has become an indispensable whole of the modern world. With the rapid development of technology, more data has begun to be stored in our world, where the use of computers is increasing. These big data do not mean anything on their own. However, they gain a meaningful dimension from inferences based on certain patterns by increasing their competencies in data and analytics. There are data mining and machine learning techniques suitable for the research and data type to be made, enabling the determination of patterns. With these techniques, there are rules, namely algorithms, between the data. Data mining and machine learning techniques can be used for different purposes in many different sectors. In this study, the similarities and differences between data mining and machine learning have been tried to be revealed and these disciplines; It has been tried to determine the common and divergent points with data science, statistics and other disciplines. In addition, using the data of a textile company producing trousers, multiple linear regression, artificial neural networks and decision trees techniques were applied with the help of R Studio, Python and Knime machine learning programs, and estimated model results were found and model performances were compared. As a result of the study, it was concluded that the best algorithm in predicting success is artificial neural networks and the best machine learning program is RStudio.

Keywords

• Textile Sector
• Machine learning
• Multiple Linear Regression
• Neural Networks
• Decision Trees
• Python
• RStudio
• Knime

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