Abstract
The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75-25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree.
I. INTRODUCTION
Today, data mining is used in the solution of problems in many fields such as health, finance and education. Data mining studies are being carried out in the field of health for diagnosis of the disease, in customer-oriented industries such as telecommunication, insurance and banking to work on customer churn and customer acquisition. In this research, a forecasting study was carried out to see whether the campaign of a bank results in new customer acquisition. Another purpose of this study is to see the results of the same classification algorithms in different data mining programs. Obtained results were shown in tables in the results section.
