Submit Your Paper
  1. Home
  2. Journals
  3. Fusion: Practice and Applications

Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Fusion: Practice and Applications

Volume 14 , Issue 2 , PP: 159-171, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

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

Elvir Akhmetshin 1 * , Nurulla Fayzullaev 2 , Elena Klochko 3 , Denis Shakhov 4 , Valentina Lobanova 5

  • 1 Candidate of Economic Sciences, Associate Professor of Department of Economics and Management, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia - (elvir@mail.ru)
  • 2 Candidate of Economic Sciences, Associate Professor, Head of Department of Accounting and Audit, Urgench State University, Urgench, Uzbekistan - (fayzullaev.n.b@mail.ru)
  • 3 Doctor of Economic Sciences, Professor of Department of Management, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, Russia - (klochko.e.n@yandex.ru)
  • 4 Candidate of Sociological Sciences, Associate Professor of Department of Economics and Management, Khorezm University, Urgench, Uzbekistan - (d.a.shkhv@gmail.com)
  • 5 Candidate of Economic Sciences, Associate Professor of Department of Enterprise Economics, Regional and Personnel Management, Kuban State University, Krasnodar, Russia - (valentina.v.lobanova@list.ru)

    • Doi: https://doi.org/10.54216/FPA.140213

    Received: July 16, 2023 Revised: November 22, 2023 Accepted: January 19, 2024
    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.

    Keywords :

    Intelligent systems , Data analytics , Customer churn prediction , Feature selection , Machine learning

    References

    [1]     Wahul, R.M., Kale, A.P. and Kota, P.N., 2023. An Ensemble Learning Approach to Enhance Customer Churn Prediction in Telecom Industry. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp.258-266.

    [2]     Fujo, S.W., Subramanian, S. and Khder, M.A., 2022. Customer churn prediction in the telecommunication industry using deep learning. Information Sciences Letters, 11(1), p.24.

    [3]     Larasati, A., Ramadhanti, D., Chen, Y.W. and Muid, A., 2021, October. Optimizing Deep Learning ANN Model to Predict Customer Churn. In 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 1-5). IEEE.

    [4]     Thorat, M.A.S. and Sonawane, V.R., 2023. CUSTOMER CHURN PREDICTION IN TELECOMMUNICATION INDUSTRY USING DEEP LEARNING. Journal of Data Acquisition and Processing, 38(3), p.1417.

    [5]     Sudharsan, R. and Ganesh, E.N., 2022. A Swish RNN-based customer churn prediction for the telecom industry with a novel feature selection strategy. Connection Science, 34(1), pp.1855-1876.

    [6]     Motevali, M., 2023. Predicting Customer Churn Based on Deep Learning, Neural Networks and Logistic Regression. Mathematical Statistician and Engineering Applications, 72(1), pp.2180-2190.

    [7]     Almufadi, N. and Qamar, A.M., 2022. Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry. Comput. Syst. Sci. Eng., 43(3), pp.1255-1270.

    [8]     Kiruthika, J.K., Neshamoney, B.S., Kumar, L.M. and Mugunthan, V., 2021, July. Churn Prediction in Telecom sector using Deep Neural Network with Flask Application. In IOP Conference Series: Materials Science and Engineering (Vol. 1166, No. 1, p. 012060). IOP Publishing.

    [9]     Wahul, R.M., Kale, A.P. and Kota, P.N., 2023. An Ensemble Learning Approach to Enhance Customer Churn Prediction in Telecom Industry. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp.258-266.

    [10]   Zaky, A., Ouf, S. and Roushdy, M., 2022, March. Predicting banking customer churn based on artificial neural network. In 2022 5th International Conference on Computing and Informatics (ICCI) (pp. 132-139). IEEE.

    [11]   Jajam, N., Challa, N.P., Prasanna, K.S. and Ch, V.S.D., 2023. Arithmetic Optimization with Ensemble Deep Learning SBLSTM-RNN-IGSA model for Customer Churn Prediction. IEEE Access.

    [12]   Amatare, S.A. and Ojo, A.K., 2020. Predicting customer churn in telecommunication industry using convolutional neural network model. IOSR Journal of Computer Engineering (IOSR-JCE), 22(3), pp.54-59.

    [13]   Gabhane, M.D. and Aslam Suriya, D.S., 2022. Churn Prediction in Telecommunication Business using CNN and ANN. Journal of Positive School Psychology, pp.4672-4680.

    [14]   Abualkishik, A.Z., Almajed, R. and Thompson, W., 2023. Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms. Journal of Intelligent Systems & Internet of Things, 8(1).

    [15]   Jeyakarthic, M. and Venkatesh, S., 2020. An effective customer churn prediction model using adaptive gain with back propagation neural network in cloud computing environment. Journal of Research on the Lepidoptera, 51(1), pp.386-399.

    [16]   Pekel Ozmen, E. and Ozcan, T., 2022. A novel deep learning model based on convolutional neural networks for employee churn prediction. Journal of Forecasting, 41(3), pp.539-550.

    [17]   Garimella, B., Prasad, G.V.S.N.R.V. and Prasad, M.K., 2021. Churn prediction using optimized deep learning classifier on huge telecom data. Journal of Ambient Intelligence and Humanized Computing, pp.1-22.

    [18]   Amin, A., Adnan, A. and Anwar, S., 2023. An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes. Applied Soft Computing, 137, p.110103.

    [19]   Hussen, N., Elghamrawy, S.M., Salem, M. and El-Desouky, A.I., 2023. A Fully Streaming Big Data Framework for Cyber Security based on Optimized Deep Learning Algorithm. IEEE Access.

    [20]   Alham, M.H., Gad, M.F. and Ibrahim, D.K., 2023. Potential of wind energy and economic assessment in Egypt considering optimal hub height by equilibrium optimizer. Ain Shams Engineering Journal, 14(1), p.101816.

    [21]   Mafarja, M., Thaher, T., Al-Betar, M.A., Too, J., Awadallah, M.A., Abu Doush, I. and Turabieh, H., 2023. Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. Applied Intelligence, pp.1-43.

    [22]   Seyfioğlu, M.S., Özbayoğlu, A.M. and Gürbüz, S.Z., 2018. Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Transactions on Aerospace and Electronic Systems, 54(4), pp.1709-1723.

    [23]   Zhang, Y., Li, T., Li, Y. and Wang, G., 2023. Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker. Aerospace, 10(9), p.806.

    [24]   https://www.kaggle.com/datasets/arashnic/telecom-churn-dataset

    [25]   Abdullaev, I., Prodanova, N., Ahmed, M.A., Lydia, E.L., Shrestha, B., Joshi, G.P. and Cho, W., 2023. Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries. Electronic Research Archive, 31(8), pp.4443-4458.

    Cite This Article As :
    Akhmetshin, Elvir. , Fayzullaev, Nurulla. , Klochko, Elena. , Shakhov, Denis. , Lobanova, Valentina. Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications, vol. , no. , 2024, pp. 159-171. DOI: https://doi.org/10.54216/FPA.140213
    Akhmetshin, E. Fayzullaev, N. Klochko, E. Shakhov, D. Lobanova, V. (2024). Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications, (), 159-171. DOI: https://doi.org/10.54216/FPA.140213
    Akhmetshin, Elvir. Fayzullaev, Nurulla. Klochko, Elena. Shakhov, Denis. Lobanova, Valentina. Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications , no. (2024): 159-171. DOI: https://doi.org/10.54216/FPA.140213
    Akhmetshin, E. , Fayzullaev, N. , Klochko, E. , Shakhov, D. , Lobanova, V. (2024) . Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications , () , 159-171 . DOI: https://doi.org/10.54216/FPA.140213
    Akhmetshin E. , Fayzullaev N. , Klochko E. , Shakhov D. , Lobanova V. [2024]. Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications. (): 159-171. DOI: https://doi.org/10.54216/FPA.140213
    Akhmetshin, E. Fayzullaev, N. Klochko, E. Shakhov, D. Lobanova, V. "Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction," Fusion: Practice and Applications, vol. , no. , pp. 159-171, 2024. DOI: https://doi.org/10.54216/FPA.140213

    Article Statistics

    View
    55572
    Download
    764

    Download