Predicting Customers Churn in a Relational Database
This paper explores how two main classical classification models work and generate predictions through a commercial solution of relational database management system (Microsoft SQL Server 2012). The aim of the paper is to accurately predict churn among a set of customers defined by various discrete...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Inforec Association
2014-01-01
|
| Series: | Informatică economică |
| Subjects: | |
| Online Access: | http://revistaie.ase.ro/content/71/01%20-%20Cimpoeru,%20Andreescu.pdf |
| _version_ | 1818992834008580096 |
|---|---|
| author | Catalin CIMPOERU Anca Ioana ANDREESCU |
| author_facet | Catalin CIMPOERU Anca Ioana ANDREESCU |
| author_sort | Catalin CIMPOERU |
| collection | DOAJ |
| description | This paper explores how two main classical classification models work and generate predictions through a commercial solution of relational database management system (Microsoft SQL Server 2012). The aim of the paper is to accurately predict churn among a set of customers defined by various discrete and continuous variables, derived from three main data sources: the commercial transactions history; the users’ behavior or events happening on their computers; the specific identity information provided by the customers themselves. On a theoretical side, the paper presents the main concepts and ideas underlying the Decision Tree and Naïve Bayes classifiers and exemplifies some of them with actual hand-made calculations of the data being modeled by the software. On an analytical and practical side, the paper analyzes the graphs and tables generated by the classifying models and also reveal the main data insights. In the end, the classifiers’ accuracy is evaluated based on the test data method. The most accurate one is chosen for generating predictions on the customers’ data where the values of the response variable are not known. |
| first_indexed | 2024-12-20T20:32:27Z |
| format | Article |
| id | doaj.art-5a8cb8fc7027425f80d19ff3a1a96925 |
| institution | Directory Open Access Journal |
| issn | 1453-1305 1842-8088 |
| language | English |
| last_indexed | 2024-12-20T20:32:27Z |
| publishDate | 2014-01-01 |
| publisher | Inforec Association |
| record_format | Article |
| series | Informatică economică |
| spelling | doaj.art-5a8cb8fc7027425f80d19ff3a1a969252022-12-21T19:27:19ZengInforec AssociationInformatică economică1453-13051842-80882014-01-0118351610.12948/issn14531305/18.3.2014.01Predicting Customers Churn in a Relational DatabaseCatalin CIMPOERUAnca Ioana ANDREESCUThis paper explores how two main classical classification models work and generate predictions through a commercial solution of relational database management system (Microsoft SQL Server 2012). The aim of the paper is to accurately predict churn among a set of customers defined by various discrete and continuous variables, derived from three main data sources: the commercial transactions history; the users’ behavior or events happening on their computers; the specific identity information provided by the customers themselves. On a theoretical side, the paper presents the main concepts and ideas underlying the Decision Tree and Naïve Bayes classifiers and exemplifies some of them with actual hand-made calculations of the data being modeled by the software. On an analytical and practical side, the paper analyzes the graphs and tables generated by the classifying models and also reveal the main data insights. In the end, the classifiers’ accuracy is evaluated based on the test data method. The most accurate one is chosen for generating predictions on the customers’ data where the values of the response variable are not known.http://revistaie.ase.ro/content/71/01%20-%20Cimpoeru,%20Andreescu.pdfData MiningPredictive AnalyticsClassificationDecision TreeNaïve BayesChurn AnalysisMicrosoft SQL Server |
| spellingShingle | Catalin CIMPOERU Anca Ioana ANDREESCU Predicting Customers Churn in a Relational Database Informatică economică Data Mining Predictive Analytics Classification Decision Tree Naïve Bayes Churn Analysis Microsoft SQL Server |
| title | Predicting Customers Churn in a Relational Database |
| title_full | Predicting Customers Churn in a Relational Database |
| title_fullStr | Predicting Customers Churn in a Relational Database |
| title_full_unstemmed | Predicting Customers Churn in a Relational Database |
| title_short | Predicting Customers Churn in a Relational Database |
| title_sort | predicting customers churn in a relational database |
| topic | Data Mining Predictive Analytics Classification Decision Tree Naïve Bayes Churn Analysis Microsoft SQL Server |
| url | http://revistaie.ase.ro/content/71/01%20-%20Cimpoeru,%20Andreescu.pdf |
| work_keys_str_mv | AT catalincimpoeru predictingcustomerschurninarelationaldatabase AT ancaioanaandreescu predictingcustomerschurninarelationaldatabase |