Classification and analysis of customer data using a novel criterion based random forest algorithm to improve retention rate over SVM algorithm in terms of prediction rate
The framework aims at analyzing the telecom customer data and predicting the churn for improving the customer retention rate. The customer information dataset used for training and testing of the proposed prediction model consists of 7043 customers with 21 attributes. The Prediction of Churn is done...
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creator | Pepakayala, Sai Surya Kannan, Anitha |
description | The framework aims at analyzing the telecom customer data and predicting the churn for improving the customer retention rate. The customer information dataset used for training and testing of the proposed prediction model consists of 7043 customers with 21 attributes. The Prediction of Churn is done by adapting Random Forest (RF) and Support Vector Machine (SVM) algorithms. The classification accuracy of the Random Forest classifier is (79%) and SVM is (75%). There is a statistically significant difference among the study groups with a significance value (p |
doi_str_mv | 10.1063/5.0114284 |
format | Conference Proceeding |
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The customer information dataset used for training and testing of the proposed prediction model consists of 7043 customers with 21 attributes. The Prediction of Churn is done by adapting Random Forest (RF) and Support Vector Machine (SVM) algorithms. The classification accuracy of the Random Forest classifier is (79%) and SVM is (75%). There is a statistically significant difference among the study groups with a significance value (p<0.05). The experimental results provide evidence that the Random Forest classifier seems to be more suitable for churn prediction with a lesser error rate, better sensitivity, and accuracy.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0114284</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Classification ; Classifiers ; Customer satisfaction ; Prediction models ; Support vector machines</subject><ispartof>AIP conference proceedings, 2023, Vol.2655 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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The customer information dataset used for training and testing of the proposed prediction model consists of 7043 customers with 21 attributes. The Prediction of Churn is done by adapting Random Forest (RF) and Support Vector Machine (SVM) algorithms. The classification accuracy of the Random Forest classifier is (79%) and SVM is (75%). There is a statistically significant difference among the study groups with a significance value (p<0.05). The experimental results provide evidence that the Random Forest classifier seems to be more suitable for churn prediction with a lesser error rate, better sensitivity, and accuracy.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Customer satisfaction</subject><subject>Prediction models</subject><subject>Support vector machines</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kctKxDAUhoMoOI4ufIMD7oSOSdq0zVIGbzDiwgvuStokY4a2qUlmYJ7JlzRzQV25OAQO3_-fk_8gdE7whOA8vWITTEhGy-wAjQhjJClykh-iEcY8S2iWvh-jE-8XGFNeFOUIfU1b4b3RphHB2B5EL2OJdu2NB6uhWfpgO-VAiiBg6U0_BwG9XakWGmeCchtVLbyS4KLYdqCtUz6AaOc2Ah8dBAumG1zUgFNB9dtBTgQFseXg-e3xD2x6iKbddvjglDTND36KjrRovTrbv2P0envzMr1PZk93D9PrWTKQvAwJL7K6ZpjTBuOaNKrQrGYS6zqnmHPJBdY6VULLTVJlwwinBKdUyZyWJaMsHaOLnW_c-XMZ_1It7NLFUHxFy-jLOCuLSF3uKN-YsA2vGpzphFtXK-sqVu0PUQ1S_wcTXG0u9ytIvwFJIY48</recordid><startdate>20230504</startdate><enddate>20230504</enddate><creator>Pepakayala, Sai Surya</creator><creator>Kannan, Anitha</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20230504</creationdate><title>Classification and analysis of customer data using a novel criterion based random forest algorithm to improve retention rate over SVM algorithm in terms of prediction rate</title><author>Pepakayala, Sai Surya ; Kannan, Anitha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p168t-974bb5092c00b1ce7f5b5d0fb62099d9a0ff3eafd01148c51921032ed62885253</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Customer satisfaction</topic><topic>Prediction models</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pepakayala, Sai Surya</creatorcontrib><creatorcontrib>Kannan, Anitha</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pepakayala, Sai Surya</au><au>Kannan, Anitha</au><au>Iqba, Uqbah</au><au>Aravindan, Surendar</au><au>Krit, Salahddine</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Classification and analysis of customer data using a novel criterion based random forest algorithm to improve retention rate over SVM algorithm in terms of prediction rate</atitle><btitle>AIP conference proceedings</btitle><date>2023-05-04</date><risdate>2023</risdate><volume>2655</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The framework aims at analyzing the telecom customer data and predicting the churn for improving the customer retention rate. The customer information dataset used for training and testing of the proposed prediction model consists of 7043 customers with 21 attributes. The Prediction of Churn is done by adapting Random Forest (RF) and Support Vector Machine (SVM) algorithms. The classification accuracy of the Random Forest classifier is (79%) and SVM is (75%). There is a statistically significant difference among the study groups with a significance value (p<0.05). The experimental results provide evidence that the Random Forest classifier seems to be more suitable for churn prediction with a lesser error rate, better sensitivity, and accuracy.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0114284</doi><tpages>9</tpages></addata></record> |
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subjects | Algorithms Classification Classifiers Customer satisfaction Prediction models Support vector machines |
title | Classification and analysis of customer data using a novel criterion based random forest algorithm to improve retention rate over SVM algorithm in terms of prediction rate |
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