Data Mining for Churn Prediction: Multiple Regressions Approach

Awang, Mohd Khalid; Rahman, Mohd Nordin Abdul; Ismail, Mohammad Ridwan

doi:10.1007/978-3-642-35603-2_47

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…However, further details can be found in previous works related to ANN (Aleksander and Morton, 1990;Ilonen et al, 2003) or to SVM (Cristianini and Shawe-Taylor, 2000;Dibike et al, 2001;Vapnik, 1998). Simpler methods for data analysis, like multiple regressions (MR), can be used in data mining (Awang et al, 2012). However, they were not included in this study since they showed a poor performance in comparison with ANN and SVM (i.e., MR models presented higher errors than ANN and SVM, which may indicate that they are unable to capture the nonlinear relationships between the variables used in this work).…”

Section: Data Mining Methodsmentioning

confidence: 99%

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

Araujo

Palha

Martins

et al. 2019

Transportation Research Part D: Transport and Environment

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The energy or fuel consumption of the millions of vehicles that daily operate in road pavements has a significant economic and environmental impact on the use phase of road infrastructures regarding their life cycle analysis. Therefore, new solutions should be studied to reduce the vehicles energy consumption, namely due to the tire-pavement interaction, and contribute towards the sustainable development. This study aims at estimating the energy consumption due to the rolling resistance of tires moving over pavements with distinct surface characteristics. Thus, different types of asphalt mixtures were used in the surface course to determine the main parameters influencing the energy consumption. A laboratory scale prototype was developed explicitly for this evaluation. Data mining techniques were used to analyze the experimental results due to the complex correlation between the data collected during the tests, providing meaningful results. In particular, the artificial neural network allowed to obtain models with excellent capacity to estimate energy consumption. A sensitive analysis was carried out with a five input parameter model, which showed that the main parameters controlling the energy consumption are the vehicle speed and the mean texture depth.

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Section: Data Mining Methodsmentioning

confidence: 99%

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

Araujo

Palha

Martins

et al. 2019

Transportation Research Part D: Transport and Environment

View full text Add to dashboard Cite

show abstract

“…In some previous researches, supervised learning approaches were used to identify churn like Naive Bayes, Logistic Regression, Support Vector Machines, Decision Tree and Random Forest (e.g., [27][28][29]). Awang et al [30] presented a regression-based churn prediction model. This model utilizes the customer's feature data for analysis and churns identification.…”

Section: Classifier Based Churn Predictionmentioning

confidence: 99%

A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Arshad¹,

Iqbal²,

Naz³

et al. 2022

Computers, Materials &Amp; Continua

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Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the imbalanced nature of the dataset are the other main challenges for an accurate prediction. In this paper, we propose a hybrid model, name as "A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning (HCPRs)" that used Synthetic Minority Over-Sampling Technique (SMOTE) and Particle Swarm Optimization (PSO) to address the issue of imbalance class data and feature selection. Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities. Substantial experiments are performed to test and validate the model on Random Forest (RF), Linear Regression (LR), Naïve Bayes (NB) and XG-Boost. Results show that the proposed model when used with XGBoost classifier, has greater Accuracy Under Curve (AUC) of 98% as compared with other methods.

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“…Ahmed [7] used a hybrid firefly algorithm based approach for churn prediction of large telecommunication data, using firefly algorithm for identifying optimal solutions and combining simulated annealing algorithm with firefly algorithm for optimization. Awnag et al [8] proposed a regression based churn prediction model to identify customer churn by using multiple regression analysis, this technique uses customer feature data for analysis and provides good performance. Sara Tavassoli et al [9] proposed three hybrid integrated classifiers based on bagging and boosting, the proposed method can be applied not only for customer churn prediction but also for any other binary classification algorithm application.…”

Section: Introductionmentioning

confidence: 99%

Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP

Peng¹,

Peng²

2022

JCC

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To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN algorithm for data processing on the unbalanced sample set; based on the GA-XGBoost model, the XGBoost algorithm is used to construct the telecom customer churn prediction model, and the hyperparameters of the model are optimized by using the genetic algorithm. The experimental results show that compared with traditional machine learning methods such as GBDT, decision tree, KNN and single XGBoost model, the improved XGBoost model has better performance in recall, F1 value and AUC value; the GA-XGBoost model is integrated with SHAP framework to analyze and explain the important features affecting telecom customer churn, which is more in line with the telecom industry to predict customer the actual situation of churn.

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Data Mining for Churn Prediction: Multiple Regressions Approach

Cited by 10 publications

References 3 publications

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques

A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP

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