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Churn Prediction Using MAPREDUCE


Affiliations
1 Department of CSE, B. I. S. T. (Bharath University), Chennai, TN, India
 

The data mining process to identify churners has concern with size of the dataset. This paper analyzes the telecom customer complaints and call quality datasets using Mapreduce to predict the customer churn. HDFS and Mapreduce make it possible to mine larger data sets without the constraints of the data size.

Keywords

Hadoop MapReduce, Telecommunication, Churn Analysis, Data Mining.
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  • Churn Prediction Using MAPREDUCE

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Authors

S. Ezhilmathi Sonia
Department of CSE, B. I. S. T. (Bharath University), Chennai, TN, India
S. Brintha Rajakumar
Department of CSE, B. I. S. T. (Bharath University), Chennai, TN, India
C. Nalini
Department of CSE, B. I. S. T. (Bharath University), Chennai, TN, India

Abstract


The data mining process to identify churners has concern with size of the dataset. This paper analyzes the telecom customer complaints and call quality datasets using Mapreduce to predict the customer churn. HDFS and Mapreduce make it possible to mine larger data sets without the constraints of the data size.

Keywords


Hadoop MapReduce, Telecommunication, Churn Analysis, Data Mining.