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Customer purchasing behavior prediction using machine learning classification techniques

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Abstract

Many sales and service-providing companies need to talk up related customers while launching the new products, services, and updated versions of existing products. While doing so, they need to target their existing customers. The behavior of these customers gives companies information about how to sell products. This paper presents a comparative study of different machine learning techniques that have been applied to the problem of customer purchasing behavior prediction. Experiments are done using supervised classification machine learning techniques like logistic regression, decision tree, k-nearest neighbors (KNN), Naïve Bayes, SVM, random forest, stochastic gradient descent (SGD), ANN, AdaBoost, XgBoost, and dummy classifier, as well as some hybrid algorithms that use stacking like SvmAda, RfAda, and KnnSgd. Models are evaluated using the cross-validation technique. Furthermore, the confusion matrix and ROC curve are used to calculate the accuracy of each model. Finally, the best classifier is a hybrid classifier using the ensemble stacking technique (KnnSgd), with an accuracy of 92.42%. KnnSgd gives the highest accuracy with maximum features because the error of the KNN and SGD are minimized by the KNN at the end.

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Authors and Affiliations

  1. Department of Information Technology, Rajkiya Engineering College, Atarra, Banda, 210201, UP, India

    Gyanendra Chaubey & Siddhartha Kumar Arjaria

  2. Dr. D Y Patil School of Engineering and Technology, Charoli (BK), via Lohegaon, Pune, 412105, Maharastra, India

    Prathamesh Rajendra Gavhane

  3. Department of Information Technology, Madhav Institute of Technology and Science, Gwalior, 474005, MP, India

    Dhananjay Bisen

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  1. Gyanendra Chaubey
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  2. Prathamesh Rajendra Gavhane
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  3. Dhananjay Bisen
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  4. Siddhartha Kumar Arjaria
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Correspondence to Gyanendra Chaubey.

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Chaubey, G., Gavhane, P.R., Bisen, D. et al. Customer purchasing behavior prediction using machine learning classification techniques. J Ambient Intell Human Comput 14, 16133–16157 (2023). https://doi.org/10.1007/s12652-022-03837-6

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  • DOI: https://doi.org/10.1007/s12652-022-03837-6

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