A Comparative Analysis Using RStudio for Churn Prediction
Vani Kapoor Nijhawan1, Mamta Madan2, Meenu Dave3
1Vani Kapoor Nijhawan, Assistant Professor, Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, formerly Indraprastha University, Delhi.
2Mamta Madan, Professor, Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, formerly Indraprastha University, Delhi.
3Meenu, Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, formerly Indraprastha University, Delhi.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 02 June 2019 | PP: 324-326 | Volume-8 Issue-7S2 May 2019 | Retrieval Number: G10560587S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the availability of numerous data, in each and every sphere, it has become significant to analyze the voluminous data, and utilize the generated patterns for the future predictions. This is what we refer to as data mining. This paper exploits, decision tree technique, to predict churning trends of telecom users. For this study, authors are making use of R and its GUI Rattle. In this paper, the focus is, to compare the variations in churning patterns of a number of users, based on the reflections made by different variables or factors and then make the predictions thereafter.
Keywords: Data Mining, Decision Tree, Customer Churn, RStudio, Rattle.
Scope of the Article: Data Mining and Warehousing