Semantic Scholar Open Access 2020 124 sitasi

Churn Prediction in Telecommunication using Logistic Regression and Logit Boost

Hemlata Jain A. Khunteta S. Srivastava

Lihat Sumber DOI

Abstrak

Abstract Today in every industry weather, it is ISP, IT products, social network or mobile services there is the problem of customer churn (Customers changing their services from one service provider to another). However, in telecommunication the customers churning very frequently. As the market in telecom is fiercely competitive, in that case, companies proactively have to determine the customers churn by analyzing their behavior and try to put effort and money in retaining the customers. In this proposed model, two machine-learning techniques were used for predicting customer churn Logistic regression and Logit Boost. Experiment was carried out in the WEKA Machine-learning tool, along with a real database from an American company Orange. The result were shown in different evaluation measures.

Topik & Kata Kunci

Computer Science

Penulis (3)

Hemlata Jain

A. Khunteta

S. Srivastava

Format Sitasi

APA MLA BibTeX

Jain, H., Khunteta, A., Srivastava, S. (2020). Churn Prediction in Telecommunication using Logistic Regression and Logit Boost. https://doi.org/10.1016/j.procs.2020.03.187

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.procs.2020.03.187

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 124×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.procs.2020.03.187
Akses: Open Access ✓