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

Awang, Mohd Khalid; Ismail, Mohammad Ridwan; Makhtar, Mokhairi; Rahman, Mohd Nordin Abdul; Mamat, Abd Rasid

doi:10.14419/ijet.v7i2.15.11196

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Decision tree Neural networks [39] Deep learning Gradient boosted tree KNN Naive Bayes Neural networks [40] Artificial neural networks [41], [42] Feed-forward artificial neural networks Partial swarm optimization [43] Decision tree Regression Neural networks [44] Multi-layer neural networks [45] Decision trees Logistics regression Artificial neural networks [46] Decision trees [47], [ This study aims to establish models that predict the customers to churn with machine learning methods on a data set with 21 attributes for 7043 customers in the telecommunication sector and compare these models. In this way, companies will be able to offer various campaigns and strategies to their customers who are likely to churn and increase their customers' loyalty to the company.…”

Section: Customer Churn Predictionmentioning

confidence: 99%

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

BİLİŞİK,

SARP

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

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Section: Customer Churn Predictionmentioning

confidence: 99%

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

BİLİŞİK,

SARP

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature

Freire,

Santos Mauricio Sanchez,

Castillo Sequera

et al. 2024

IEEE Access

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A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

Mandal

Ghosh

Shit

et al. 2020

Vietnam J. Comp. Sci.

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Various training algorithms are used in artificial neural networks for updating the weights during training the network. But, the selection of the appropriate training algorithm is dependent on the input–output mapping of dataset for which the network is constructed. In this paper, a framework has been proposed consisting of five modules to select the optimal training algorithm for predicting pig breeds from their images. The individual pig images from five pig-breeds have been captured using inbuilt camera of mobile phone and the contour of pig has been segmented from each captured image by HUE-based segmentation algorithm. In Statistical Parameter and Color Component retrieval module, parameters like entropy, standard deviation, variance, mean, median, and mode and color properties like hue, saturation, value (HSV) extracted from the content of each segmented image. Values of all extracted parameters have been transferred into Training Algorithm Selection Module. In this module, a fitting neural network with different numbers of hidden neurons has been executed by feeding all extracted values from pig images for mapping their breeds. Ten training algorithms have been applied on the same extracted dataset separately for five epochs each keeping other network parameters constants. The mean square error (MSE) and correlation coefficient ([Formula: see text]) for the validation set have been calculated after adjustment of weights and biases in each connection of the neurons. One training algorithm among 10 and its suitable number of hidden neurons has been selected based on comparative analysis for getting lower MSE and higher [Formula: see text] in the validation set. Then, the fitting network with selected training algorithm has been run on the same extracted datasets until the stopping condition is reached. Then the test set images are fed into the network and the network output has been categorized to class which has been assigned to each breed of pig in Breed Prediction Module. The proposed framework has been able to predict breeds with 96.00% accuracy, achieved by the trial with 50 images of the test set. It may be concluded that the Neuro Statistic Neural Network model may be used for breed prediction of pigs by using images of individual pigs.

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

Cited by 5 publications

References 7 publications

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature

A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

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