scholarly journals A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction

2016 ◽  
pp. 183-196
Author(s):  
Bingquan Huang ◽  
Ying Huang ◽  
Chongcheng Chen ◽  
M. -T. Kechadi
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Churn Prediction ◽  
Rule Based ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction

2018 ◽  
Vol 7 (2.15) ◽  
pp. 35 ◽  
Author(s):  
Mohd Khalid Awang ◽  
Mohammad Ridwan Ismail ◽  
Mokhairi Makhtar ◽  
M Nordin A Rahman ◽  
Abd Rasid Mamat
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Telecommunication Service ◽  
Churn Prediction ◽  
Data Set ◽  
Customer Churn ◽  
The Neural Network ◽  
Customer Churn Prediction

Predicting customer churn has become the priority of every telecommunication service provider as the market  is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction.  The data set used to train and test the neural network algorithms was provided by one of the leading telecommunication company in Malaysia. The Multilayer Perceptron (MLP) networks are trained using nine (9) types of learning algorithms, which are Levenberg Marquardt backpropagation (trainlm), BFGS Quasi-Newton backpropagation (trainbfg), Conjugate Gradient backpropagation with Fletcher-Reeves Updates (traincgf), Conjugate Gradient backpropagation with Polak-Ribiere Updates (traincgp), Conjugate Gradient backpropagation with Powell-Beale Restarts (traincgb), Scaled Conjugate Gradient backpropagation (trainscg), One Step Secant backpropagation (trainoss), Bayesian Regularization backpropagation (trainbr), and Resilient backpropagation (trainrp). The performance of the Neural Network is measured based on the prediction accuracy of the learning and testing phases. LM learning algorithm is found to be the optimum model of a neural network model consisting of fourteen input units, one hidden node and one output node. The best result of the experiment indicated that this model is able to produce the performance accuracy of 94.82%. 

A SURVEY OF EMPLOYEE AND CUSTOMER CHURN PREDICTION METHODOLOGIES

2020 ◽  
Vol 9 (6) ◽  
pp. 3955-3962
Author(s):  
S. Madane ◽  
D. Chitre
Keyword(s):  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

Customer Churn Prediction Embedded in an Analytical CRM Model

2015 ◽  
Author(s):  
Ede Lázár
Keyword(s):  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

Data Mining Techniques in Customer Churn Prediction

2010 ◽  
Vol 3 (1) ◽  
pp. 28-32 ◽  
Author(s):  
Chih-Fong Tsai ◽  
Yu-Hsin Lu
Keyword(s):  
Data Mining ◽  
Churn Prediction ◽  
Data Mining Techniques ◽  
Customer Churn ◽  
Customer Churn Prediction

A comparison of machine learning techniques for customer churn prediction

2015 ◽  
Vol 55 ◽  
pp. 1-9 ◽  
Author(s):  
T. Vafeiadis ◽  
K.I. Diamantaras ◽  
G. Sarigiannidis ◽  
K.Ch. Chatzisavvas
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction
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