Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms

Kavitha, V.; Kumar, G. Hemantha; Kumar, Sumant; M, Harish

doi:10.17577/ijertv9is050022

Cited by 29 publications

(14 citation statements)

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“…XGBoost is the abbreviation for extreme Gradient Boosting. The primary purpose of using XGBoost is due to its execution speed [16]. Gradient descent helps in minimising the differentiable function but in gradient boosting the average gradient components will be computed.…”

Section: • Xgboostmentioning

confidence: 99%

Telecom Churn Prediction Using Seven Machine Learning Experiments integrating Features engineering and Normalization

Jain

Khunteta

2021

Preprint

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Machine Learning and Deep learning classification has become an important topic in the area of Telecom Churn Prediction. Researchers have come out with very efficient experiments for Churn Prediction and have given a new direction to the telecommunication Industry to save their customers. Companies are eagerly developing the models for predicting churn and putting their efforts to save the potential churners. Therefore, for a better churn prediction model, finding the factors of churn is very important. This study is aiming to find the factors of user’s churn by evaluating their past service usage details. For this purpose, study is taking the advantage of feature importance, feature normalisation, feature correlation and feature extraction. After feature selection and extraction this study performing seven different experiments on the dataset to bring out the best results and compared the techniques. First Experiment includes a hybrid model of Decision tree and Logistic Regression, second experiment include PCA with Logistic Regression and Logit Boost, third experiment using a Deep Learning Technique that is CNN-VAE (Convolutional Neural Network with Variational Autoencoder), Fourth, fifth, sixth and seventh experiments was done on Logistic Regression, Logit Boost, XGBoost and Random Forest respectively. First four experiments are hybrid models and rest are using standalone techniques. The Orange dataset was used in this technique which has 3333 subscriber’s entries and 21 features. On the other hand, these experiments are compared with already existing models that have been developed in literature studies. The performance was evaluated using Accuracy, Precision, Recall rate, F-measure, Confusion Matrix, Marco Average and Weighted Average. This study proved to get better results as compared to old models. Random Forest outperformed in this study by achieving 95% Accuracy and all other experiments also produced very good results. The study states the importance of data mining techniques for a churn prediction model and proposes a very good comparison model where all machine Learning Standalone techniques, Deep Learning Technique and hybrid models with Feature Extraction tasks are being used and compared on the same dataset to evaluate the techniques performance better.

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Section: • Xgboostmentioning

confidence: 99%

Telecom Churn Prediction Using Seven Machine Learning Experiments integrating Features engineering and Normalization

Jain

Khunteta

2021

Preprint

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“…Hal ini tentunya merupakan suatu masalah bagi perusahaan tersebut. Untuk mempertahankan pelanggannya, perusahaan telekomunikasi harus meningkatkan produk dan layanannya, serta terlebih dahulu mengetahui pelanggan yang memiliki perilaku yang berkemungkinan akan meninggalkan layanan dari perusahaan (Kavitha, G. Kumar, S. Kumar, & Harish, 2020). Prediksi customer churn merupakan cara untuk mengidentifikasi churners sebelum berpindah, yang dapat dilakukan dengan cara menganalisis data dan menemukan pola yang berguna.…”

Section: Pendahuluanunclassified

“…Dalam melakukan prediksi, pendekatan ini memerlukan data-data masa lalu yang telah dikumpulkan (Yulianti, 2018). Pada beberapa kasus mengenai prediksi customer churn, teknik klasifikasi yang umum digunakan yaitu decision tree, rule-based learning, dan neural networks yang terbukti bahwa ketiga algoritme tersebut dapat melakukan prediksi terhadap permasalahan customer churn (Kavitha et al, 2020). Pada penelitian sebelumnya, melakukan perbandingan terhadap tiga algoritme yaitu random forest, logistic regression, dan XGBoost untuk memprediksi customer churn.…”

Section: Pendahuluanunclassified

“…Pada penelitian sebelumnya, melakukan perbandingan terhadap tiga algoritme yaitu random forest, logistic regression, dan XGBoost untuk memprediksi customer churn. Penelitian ini mendapatkan hasil bahwa algoritme random forest memiliki akurasi sedikit lebih tinggi dari dua algorime lainnya (Kavitha et al, 2020). Penelitian lain, menggunakan algoritme decision tree untuk melakukan prediksi terhadap customer churn, selain sering digunakan pada pemodelan klasifikasi, algoritme ini juga mampu dalam memprediksi customer churn (Utami et al, 2020).…”

Section: Pendahuluanunclassified

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Uji Performa Teknik Klasifikasi untuk Memprediksi Customer Churn

Wicaksono

Anita

Padilah

2021

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Perkembangan industri telekomunikasi sangatlah cepat, hal ini dapat dilihat dari perilaku masyarakat yang menggunakan internet dalam berkomunikasi. Perilaku ini menyebabkan banyaknya perusahaan telekomunikasi dan meningkatnya internet service provider yang dapat menimbulkan persaingan antar provider. Pelanggan memiliki hak dalam memilih provider yang sesuai dan dapat beralih dari provider sebelumnya yang diartikan sebagai customer churn. Peralihan ini dapat menyebabkan berkurangnya pendapatan bagi perusahaan telekomunikasi sehingga penting untuk ditangani. Tujuan dari penelitian ini yaitu untuk mengetahui algoritme klasifikasi terbaik dan sesuai pada permasalahan customer churn. Penelitian ini dilakukan berdasarkan metode CRISP-DM sebagai alur penelitian dengan menerapkan tiga algoritme klasifikasi yaitu Logistic Regression, Decision Tree, dan Random Forest, yang dibantu dengan metode feature selection yaitu Backward Elimination untuk mengurangi variabel yang tidak signifikan. Hasil dari penelitian ini memperoleh bahwa algoritme Logistic Regression dengan Backward Elimination merupakan algoritme terbaik dengan nilai akurasi sebesar 82,23%, recall 57,22%, dan AUC sebesar 0,853 yang termasuk pada pemodelan good classification.

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“…Random Forest (RF) has been widely employed for solving different classification problems in other industries, such as botnet detection, smart meter data classification, satellite imagery, and employee turnover [10][11][12][13]. Although some researchers have applied RF technique to predict customer churn in telecom sector, the predicting results range between 67% and 87% [14][15][16][17][18], which still has a distance from being satisfied. To derive the most powerful method for detecting the risk of customer churn in the telecom sector, an optimized RF model is employed and compared with the other two advanced machine learning models-Support Vector Machines (SVM) and K-nearest neighbors (KNN) in this study.…”

Section: Introductionmentioning

confidence: 99%

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Edwine

Wang

Song

et al. 2022

Mathematical Problems in Engineering

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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.

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Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms

Cited by 29 publications

References 2 publications

Telecom Churn Prediction Using Seven Machine Learning Experiments integrating Features engineering and Normalization

Telecom Churn Prediction Using Seven Machine Learning Experiments integrating Features engineering and Normalization

Uji Performa Teknik Klasifikasi untuk Memprediksi Customer Churn

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

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