*Five Years Citation in Google scholar (2016 - 2020) is. 1451*   *    IJPR IS INDEXED IN ELSEVIER EMBASE & EBSCO *       

logo

INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence
Published by : Advanced Scientific Research
ISSN
0975-2366
Current Issue
Article In Press
No Data found.
ADOBE READER

(Require Adobe Acrobat Reader to open, If you don't have Adobe Acrobat Reader)

Index Page 1
Click here to Download
IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

Click to download
 

Article Detail

Label
Label
Hyperparameters Tuning and Model Comparison for Telecommunication Customer Churn Predictive Models

Author: KOH LI, BOOMA POOLAN MARIKANNAN
Abstract: Customer churn is a phenomenon that occurs when a customer cases doing business with a company or service. Thus, predicting customer churn is one of the primary aim of businesses in various sectors especially those which involve subscription-based services such as telecommunication sector. The markets of telecommunication sector are also very saturated which led to high competitiveness among competitors. Various researches had been conducted to develop churn predictive models utilizing various algorithms to accurately predict churn in the telecommunication sector. However, not much emphasis has been allocated in tuning the hyperparameters of the developed algorithms or models in order to further improve the performances of churn predictive models. This paper proposes the development of churn predictive models using the Naive Bayes (NB), Decision Tree (DT) as well as Artificial Neural Network (ANN) algorithms along with the incorporation of hyperparameters tuning through grid search to look for the best hyperparameters' setting which can optimize the performance of each algorithm. Grid search method manually searches a specified subset of the hyperparameter space of a machine learning algorithm and identify the best setting of hyperparameters within the designated subset which can improve the overall performance of the algorithm or model.
Keyword: Naive Bayes, Decision Tree, Artificial Neural Netowork, grid search, customer churn prediction
Download: Request For Article
 




ONLINE SUBMISSION
USER LOGIN


Username
Password
Login | Register
News & Events
SCImago Journal & Country Rank

Terms and Conditions
Disclaimer
Refund Policy
Instrucations for Subscribers
Privacy Policy

Copyrights Form

0.12
2018CiteScore
 
8th percentile
Powered by  Scopus
Google Scholar

hit counters free