Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is extended version of Artificial Neural Networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms especially with Deep Learning algorithm. Naïve Bayes, C5.0, Extreme Learning Machine and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, Synthetic Minority Oversampling Technique have been used. The results have revealed the success of Deep Learning and Decision Trees algorithms. When the data set was not balanced, the Deep Learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision and F-score have been achieved with the C5.0 algorithm.
Customers' behaviors such as tendencies, loyalty status, satisfaction criteria show an alteration day by day due to the changing world. So, these behavior changes should be analyzed very well in every step of the decision-making process. Customer churn analysis is the determination of customers who tend to leave by analyzing the customer data with various methods before this situation occurs. This study aims to develop an Extreme Learning Machine based model for customer churn prediction problem and to determine the model parameters that provide the best performance. Grid search is used for hyperparameter tuning. Also modified accuracy calculation approach has been presented. The churn data set obtained from the UCI Machine Learning Repository has been used. Naive Bayes, k-Nearest Neighbor and Support Vector Machine methods are selected for performance comparison of the model. With a value of 93.1%, the best accuracy measure has been obtained with Extreme Learning Machine. Due to the low number of parameters to be determined and performance evaluation measures that compete with other models’ results, it can be said that the Extreme Learning Machine is highly effective and interesting in the solution of the problem.
Ülkeler her ne kadar gelişmiş teknoloji, savunma ve diğer endüstri, petrol doğalgaz ve maden ürünlerinin üretimi ve ihracatı üzerine yoğunlaşmış olsa da hem ekonomik açıdan hem de en az kendi kendine yetecek düzeyde tarımsal üretimi de devam ettirmek durumdadır. Türkiye açısından durum incelenecek olursa, ülkemiz tarım faaliyetleri için oldukça uygun ve geniş alanlara sahip, kuruluşundan bugüne kadar yaygın ve etkin şekilde tarım faaliyetlerinin yürütüldüğü bir ülkedir. Türkiye’de üretilen tarım ürünlerinden birisi çaydır. Bu çalışma kapsamında, hükümet tarafından çay ekimi için izin verilen 2 il için (Artvin ve Rize) tüm ilçe ve köyleri göz önünde bulundurularak bulanık kümeleme yöntemi ile uzaklığa dayalı fabrika ve yaş çay alım yeri eşlemesi yapılmıştır. Kümeleme yaklaşımının kullanılması problemin boyutunu küçülterek daha kolay çözüm üretilmesini sağlamıştır. Analizler sonucunda bazı fabrikalara daha fazla sayıda çay alım merkezi atanmıştır. Çalışma tarımsal üretimin yanı sıra tarım ürünlerinin işlenebilmesi için de gerek üretim alanlarının dağılımı, gerek işleme tesislerinin konumlanması gibi konularda doğru bir yapılanmaya gereksinim olduğunu göstermektedir.
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