Determining the Intervening Effects of Exploratory Data Analysis and Feature Engineering in Telecoms Customer Churn Modelling
The telecoms industry is a highly competitive
sector which is constantly challenged by customer churn or
attrition. In order to remain steadfast in the consumer
business, companies need to have sophisticated churn
management strategies that will harness valuable data for
business intelligence. Data mining and machine learning are
tools which can be used by telecoms companies to monitor the
churn behaviour of customers. This study implemented
exploratory data analysis and feature engineering in a public
domain Telecoms dataset and applied seven (7) classification
techniques namely, Naïve Bayes, Generalized Linear Model,
Logistic Regression, Deep Learning, Decision Tree, Random
Forest, and Gradient Boosted Trees. The results are analyzed
using different metrics such as Accuracy, Classification error,
Precision, Recall, F1-score, and AUC. This study discussed
how these results are essential in reducing customer churn and
improving customer service. The results obtained in the
experiment demonstrate that the best classifier is Gradient
Boosted Trees. It outperforms the other classifiers in almost all
evaluation metrics. Further, all classifiers showed remarkable
improved performance after the oversampling method is
applied.
KeyWords
business intelligence, customer churn,
exploratory data analysis, feature engineering, machine learning
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