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Amaç- Makine öğrenmesi, satış tahmini alanında sıkça kullanılmaktadır. İşletmeler, yeni bir ürünü piyasaya sunmadan önce geçmiş verilerinden birtakım analizler yaparak geleceğe yönelik kestirimler yapabilmektedir. Böylelikle, optimal sayıda ürün üreterek hem hammadde ve işgücü maliyetini hem de depolama, lojistik gibi maliyetlerin ortaya çıkarabileceği zararı en aza indirmeyi amaçlarlar. Ürünün hedef kitlesini belirleyerek bu doğrultuda satış stratejileri geliştirme imkânı bulurlar. Yöntem- Bu amaçla, çalışmamızda, makine öğrenmesi problemlerinden sınıflandırma problemleri ele alınmıştır. Satılması hedeflenen ürün ile aynı özelliklere sahip, daha önce piyasaya sunulmuş ürün verileri toplanmış, “ürün satıldı” ve “ürün satılmadı” şeklinde ikili sınıflandırma çalışması yapılmıştır. Denetimli öğrenme algoritmalarından k En Yakın Komşu, Naive Bayes ve Doğrusal Destek Vektör Makineleri kullanılan çalışmada, veri seti öğrenme seti ve test seti olarak bölünmüştür. Bulgular - Çalışma sonucu olarak, 0,71 doğruluk ile k En Yakın Komşu algoritması en yüksek doğruluğu sağlamıştır. Sonuç- Satış tahmini çalışmalarında makine öğrenmesi algoritmalarından k en yakın komşu algoritması görece daha iyi sonuçlar vermektedir.
Mechanical learning is often used in the field of sales forecast. Companies can make futures by making a series of analysis of their past data before introducing a new product to the market. Thus, by producing an optimal number of products, they aim to minimize both the costs of raw materials and labour as well as the damage that can be caused by costs such as storage, logistics. By setting the target mass of the product, they find the possibility to develop sales strategies in this direction. Method- For this purpose, in our work, the problem of classification from machine learning has been addressed. The product has the same characteristics as the product intended for sale, the previously marketed product data has been collected, the "product sold" and the "product not sold" forms have been carried out. From the controlled learning algorithms k The Nearest Neighbor, Naive Bayes and Direct Support Vector Machines used in the study, the data set is divided into a learning set and a test set. Results - As a result of the study, the 0.71 accuracy of the nearest neighbor's algorithm provided the highest accuracy. In sales forecast studies, the nearest neighboring algorithm of the machine learning algorithms gives better results.
ABSTRACT Purpose- Machine learning is frequently used in the field of sales forecasting. Businesses can make predictions by analyzing some of their past data before launching a new product. Thus, by producing an optimal number of products, they aims to minimize both the cost of raw materials and labor, as well as the damage that can be instrinsed by costs such as storage and logistics. By identifying the target audience of the product, they have the opportunity to develop sales strategies in this way. Methodology- For this purpose, classification problems from machine learning problems were discussed in the study. Previously presented product data with the same characteristics as the product intended to be sold was collected, "product sold" and "product not sold" binary classification study was carried out. In the study, which used k Nearest Neighbor, Naive Bayes and Linear Support Vector Machines from supervised learning algorithms, the data set was divided into a training set and a test set. Findings- As a result of the study, the k Nearest Neighbor algorithm provided the highest accuracy with accuracy of 0.71. Conclusion- In sales prediction studies, the nearest neighbor algorithm from machine learning algorithms results in relatively better results.
Field : Sosyal, Beşeri ve İdari Bilimler
Journal Type : Ulusal
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