to download project abstract

Retaining the most valuable customers is a major problem companies face in this information age.
Especially, the field of telecommunication faces complex challenges due to a number of vibrant
competitive service providers. Therefore, it has become very difficult for them to retain existing
customers. Since the cost of acquiring new customers is much higher than the cost of retaining the
existing customers, it is the time for the telecom industries to take necessary steps to retain the
customers to stabilize their market value. CRM uses data mining (one of the elements of CRM)
techniques to interact with customers. This study investigates the use of a technique, supervised
learning, for the management and analysis of customer-related data warehouse and information.
Data mining technologies extract hidden information and knowledge from large data stored in
databases or data warehouses, which supports the corporate in decision making process. Several
data mining techniques have been proposed in the literature for predicting the happy and stressed
customer using heterogeneous customer records. Probably, the stressed customers are in the
urge of moving out to competitive service providers. This project analysis the telecom customer
data available in open data set and predict the customer stress by applying supervised machine-
learning algorithms mainly using Deep Neural Network , K Nearest Neighbour , Support Vector
Machine and Random Forest.

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