КФУ. Карточка публикации. Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction

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INTELLIGENT DATA ANALYTICS USING HYBRID GRADIENT OPTIMIZATION ALGORITHM WITH MACHINE LEARNING MODEL FOR CUSTOMER CHURN PREDICTION

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2024

Язык

английский

Авторы, сотрудники КФУ

Ахметшин Эльвир Мунирович, автор

Библиографическое описание на языке оригинала

Akhmetshin E, Fayzullaev N, Klochko E, Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.159-171.

Аннотация

Intelligent data analytics for customer churn prediction (CCP) harnesses predictive modelling algorithms, machine learning (ML) techniques, and advanced big data analytics and also uncovers the underlying drivers and patterns of churn and detects customers at risk of churning. This business strategy help organization to implement retention efforts to decrease customer attrition and proactively detect at-risk customers. CCP allows businesses to take proactive measures such as targeted marketing campaigns, personalized offers, or enhanced customer service, to maintain valuable customer and decrease revenue loss. It is widely used in industries like telecommunications, subscription services, e-commerce, and finance to optimize customer retention strategies and enhance long-term profitability. ML algorithm can detect indicator and underlying trends that precedes churn by analyzing historical customer data, including transactional patterns, behaviors, demographics, and customer interaction. The study introduces Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning (IDA-HGOAML) Model for Customer Churn Prediction. The main intention of IDA-HGOAML method focuses on the prediction and classification of customer churns and non-churns. To do so, the IDA-HGOAML technique initially undergoes data pre-processing using Z-score normalization. The IDA-HGOAML model makes use of equilibrium optimization algorithm (EOA) for the feature selection (FS). Besides, the churn prediction method is implemented by the convolutional autoencoder (CAE) model. Finally, the HGOA is exploited for the optimal hyperparameter selection of CAE model, thereby enhancing the prediction results. A widespread experimental analysis were performed to validate the enhanced efficiency of the IDA-HGOAML method. The extensive outcomes indicated the improved prediction results of the IDA-HGOAML method over existing techniques in terms of different measures.

Ключевые слова

Intelligent Data Analytics

Название журнала

Fusion: Practice and Applications

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186174417&doi=10.54216%2fFPA.140213&partnerID=40&md5=6ec11467c97aed08045161198cc0ecc9

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https://repository.kpfu.ru/?p_id=307429

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Ахметшин Эльвир Мунирович	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Akhmetshin E, Fayzullaev N, Klochko E, Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.159-171.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=307429	ru_RU
dc.description.abstract	Fusion: Practice and Applications	ru_RU
dc.description.abstract	Intelligent data analytics for customer churn prediction (CCP) harnesses predictive modelling algorithms, machine learning (ML) techniques, and advanced big data analytics and also uncovers the underlying drivers and patterns of churn and detects customers at risk of churning. This business strategy help organization to implement retention efforts to decrease customer attrition and proactively detect at-risk customers. CCP allows businesses to take proactive measures such as targeted marketing campaigns, personalized offers, or enhanced customer service, to maintain valuable customer and decrease revenue loss. It is widely used in industries like telecommunications, subscription services, e-commerce, and finance to optimize customer retention strategies and enhance long-term profitability. ML algorithm can detect indicator and underlying trends that precedes churn by analyzing historical customer data, including transactional patterns, behaviors, demographics, and customer interaction. The study introduces Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning (IDA-HGOAML) Model for Customer Churn Prediction. The main intention of IDA-HGOAML method focuses on the prediction and classification of customer churns and non-churns. To do so, the IDA-HGOAML technique initially undergoes data pre-processing using Z-score normalization. The IDA-HGOAML model makes use of equilibrium optimization algorithm (EOA) for the feature selection (FS). Besides, the churn prediction method is implemented by the convolutional autoencoder (CAE) model. Finally, the HGOA is exploited for the optimal hyperparameter selection of CAE model, thereby enhancing the prediction results. A widespread experimental analysis were performed to validate the enhanced efficiency of the IDA-HGOAML method. The extensive outcomes indicated the improved prediction results of the IDA-HGOAML method over existing techniques in terms of different measures.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU