Results 101 to 110 of about 17,717 (205)

CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction

Journal of Big Data
Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes.
I Nyoman Mahayasa Adiputra, Paweena Wanchai   +1 more
doaj   +1 more source

Research on customer churn prediction and model interpretability analysis. [PDF]

PLoS One, 2023
Peng K, Peng Y, Li W.
europepmc   +1 more source

Customer churn prediction using composite deep learning technique. [PDF]

Sci Rep, 2023
Khattak A, Mehak Z, Ahmad H, Asghar MU, Asghar MZ, Khan A.   +5 more
europepmc   +1 more source

Prediction of Customer Churn in Banking Industry

CoRR, 2020
With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six supervised classification techniques to suggest an efficient model to predict customer churn in banking industry, given ...
openaire   +2 more sources

An efficient churn prediction model using gradient boosting machine and metaheuristic optimization. [PDF]

Sci Rep, 2023
AlShourbaji I, Helian N, Sun Y, Hussien AG, Abualigah L, Elnaim B.   +5 more
europepmc   +1 more source

A proposed hybrid framework to improve the accuracy of customer churn prediction in telecom industry

Journal of Big Data
In the telecom sector, predicting customer churn has increased in importance in recent years. Developing a robust and accurate churn prediction model takes time, but it is crucial.
Shimaa Ouf, Kholoud T. Mahmoud, Manal A. Abdel-Fattah   +2 more
doaj   +1 more source

Applying CHAID for logistic regression diagnostics and classification accuracy improvement [PDF]

In this study a CHAID-based approach to detecting classification accuracy heterogeneity across segments of observations is proposed. This helps to solve some important problems, facing a model-builder: 1. How to automatically detect segments in which the
Antipov, Evgeny, Pokryshevskaya, Elena
core   +1 more source

CUSTOMER CHURN PREDICTION

IARJSET, 2021
Senthilnayaki B, Swetha M, Nivedha D
openaire   +1 more source

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries. [PDF]

Biomimetics (Basel), 2023
Mengash HA, Alruwais N, Kouki F, Singla C, Abd Elhameed ES, Mahmud A.   +5 more
europepmc   +1 more source

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

IEEE Access
The telecommunications sector has experienced exponential growth since the year 2000, reaching 5.31 trillion users by 2022, generating 1.07 trillion in revenue for telecommunications companies.
David Freire, David Santos Mauricio Sanchez, Jose Luis Castillo Sequera, Daniel Fiallo Moncayo   +3 more
doaj   +1 more source