Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386377058> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W4386377058 endingPage "161" @default.
- W4386377058 startingPage "144" @default.
- W4386377058 abstract "This study aims to develop a churn prediction model which can assist telecommunication companies in predicting customers who are most likely subject to churn. The model is developed by employing machine learning techniques on big data platforms. Customer churn is one of the most critical issues, especially in high investment telecommunication companies. Accordingly, the companies are looking for ways to predict potential customers to churn and take necessary actions to reduce the churn. To accomplish the objective of the study, it first compares eight machine learning techniques, i.e., ridge classifier, gradient booster, adaptive boosting, bagging classifier, k-nearest neighbour (kNN), decision tree, logistic regression, and random forest. By using five evaluation performance metrics (i.e., accuracy, AUC score, precision score, recall score, and the F score), kNN is selected since it outperforms other techniques. Second, the selected technique is used to predict the likelihood of customers churning." @default.
- W4386377058 created "2023-09-02" @default.
- W4386377058 creator A5038270930 @default.
- W4386377058 creator A5055913702 @default.
- W4386377058 creator A5066487372 @default.
- W4386377058 creator A5074123303 @default.
- W4386377058 creator A5077861360 @default.
- W4386377058 date "2023-08-22" @default.
- W4386377058 modified "2023-09-28" @default.
- W4386377058 title "A Machine Learning Application to Predict Customer Churn: A Case in Indonesian Telecommunication Company" @default.
- W4386377058 cites W1767272795 @default.
- W4386377058 cites W1973004077 @default.
- W4386377058 cites W2035549557 @default.
- W4386377058 cites W2069300565 @default.
- W4386377058 cites W2074780813 @default.
- W4386377058 cites W2078704831 @default.
- W4386377058 cites W2082874195 @default.
- W4386377058 cites W2088794999 @default.
- W4386377058 cites W2103699041 @default.
- W4386377058 cites W2106033090 @default.
- W4386377058 cites W2141975087 @default.
- W4386377058 cites W2143615813 @default.
- W4386377058 cites W2330418879 @default.
- W4386377058 cites W2513272121 @default.
- W4386377058 cites W2791726455 @default.
- W4386377058 cites W2796101232 @default.
- W4386377058 cites W2901532222 @default.
- W4386377058 cites W2911964244 @default.
- W4386377058 cites W2929312725 @default.
- W4386377058 cites W2981485148 @default.
- W4386377058 cites W3024604388 @default.
- W4386377058 cites W3101676849 @default.
- W4386377058 cites W3124035032 @default.
- W4386377058 cites W4200046376 @default.
- W4386377058 cites W4212883601 @default.
- W4386377058 doi "https://doi.org/10.2174/9789815124842123010013" @default.
- W4386377058 hasPublicationYear "2023" @default.
- W4386377058 type Work @default.
- W4386377058 citedByCount "0" @default.
- W4386377058 crossrefType "book-chapter" @default.
- W4386377058 hasAuthorship W4386377058A5038270930 @default.
- W4386377058 hasAuthorship W4386377058A5055913702 @default.
- W4386377058 hasAuthorship W4386377058A5066487372 @default.
- W4386377058 hasAuthorship W4386377058A5074123303 @default.
- W4386377058 hasAuthorship W4386377058A5077861360 @default.
- W4386377058 hasConcept C119857082 @default.
- W4386377058 hasConcept C145236788 @default.
- W4386377058 hasConcept C148524875 @default.
- W4386377058 hasConcept C151956035 @default.
- W4386377058 hasConcept C154945302 @default.
- W4386377058 hasConcept C161664118 @default.
- W4386377058 hasConcept C162324750 @default.
- W4386377058 hasConcept C169258074 @default.
- W4386377058 hasConcept C41008148 @default.
- W4386377058 hasConcept C45804977 @default.
- W4386377058 hasConcept C46686674 @default.
- W4386377058 hasConcept C70153297 @default.
- W4386377058 hasConcept C84525736 @default.
- W4386377058 hasConcept C95623464 @default.
- W4386377058 hasConceptScore W4386377058C119857082 @default.
- W4386377058 hasConceptScore W4386377058C145236788 @default.
- W4386377058 hasConceptScore W4386377058C148524875 @default.
- W4386377058 hasConceptScore W4386377058C151956035 @default.
- W4386377058 hasConceptScore W4386377058C154945302 @default.
- W4386377058 hasConceptScore W4386377058C161664118 @default.
- W4386377058 hasConceptScore W4386377058C162324750 @default.
- W4386377058 hasConceptScore W4386377058C169258074 @default.
- W4386377058 hasConceptScore W4386377058C41008148 @default.
- W4386377058 hasConceptScore W4386377058C45804977 @default.
- W4386377058 hasConceptScore W4386377058C46686674 @default.
- W4386377058 hasConceptScore W4386377058C70153297 @default.
- W4386377058 hasConceptScore W4386377058C84525736 @default.
- W4386377058 hasConceptScore W4386377058C95623464 @default.
- W4386377058 hasLocation W43863770581 @default.
- W4386377058 hasOpenAccess W4386377058 @default.
- W4386377058 hasPrimaryLocation W43863770581 @default.
- W4386377058 hasRelatedWork W3100297620 @default.
- W4386377058 hasRelatedWork W3126325819 @default.
- W4386377058 hasRelatedWork W3201348321 @default.
- W4386377058 hasRelatedWork W4212956667 @default.
- W4386377058 hasRelatedWork W4281866327 @default.
- W4386377058 hasRelatedWork W4281887347 @default.
- W4386377058 hasRelatedWork W4296081764 @default.
- W4386377058 hasRelatedWork W4308191010 @default.
- W4386377058 hasRelatedWork W4382701299 @default.
- W4386377058 hasRelatedWork W4383535405 @default.
- W4386377058 isParatext "false" @default.
- W4386377058 isRetracted "false" @default.
- W4386377058 workType "book-chapter" @default.