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The Implementation of a Logistic Regression Algorithm and Gradient Boosting Classifier for Predicting Telco Customer Churn

Year: 2024

Type: article

Abstract: This research aims to predict customer churn in a telecommunications company using Logistic Regression (LR) and Gradient Boosting Classifier (GBC) algorithms. Customer churn poses a significant challenge as acquiring new customers is costlier than retaining existing ones. The dataset from Kaggle comprises 7043 records and 21 attributes. The process includes data pre-processing, cleaning, transformation, and normalization using a Min-Max Scaler. The data is split into features (X) and target (y), then divided into training and testing sets with an 80:20 ratio. Both models were trained and evaluated using a confusion matrix. Results show that the GBC model outperforms the LR model, with an accuracy of 83% compared to LR's 81%. This study demonstrates the effectiveness of GBC in predicting customer churn.

Source: Pixel Jurnal Ilmiah Komputer Grafis

DOI: 10.51903/pixel.v17i1.2006

Authors: Angga Adiansya, Zaenal Abidin

Institution: State University of Semarang

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4401765151

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