PREDICTING CONSUMER PURCHASING BEHAVIOR USING MACHINE LEARNING METHODS
DOI:
https://doi.org/10.17721/3041-2323.2024.388-398Keywords:
machine learning, consumer behavior prediction, data analysis, Random Forest, Gradient Boosting, hyperparameter optimization, correlation analysis, online salesAbstract
This article explores the application of machine learning methods for predicting consumer purchasing behavior based on data analysis. Using a dataset from Kaggle, data was analyzed and prepared, including removal of duplicates, feature scaling, and correlation analysis. Various machine learning models, such as Random Forest and Gradient Boosting, were tested using different techniques, including hyperparameter optimization and class balancing. The results showed that incorporating feature correlation and hyperparameter optimization significantly improves the accuracy of the models, making them effective tools for predicting consumer behavior in online sales.
References
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Correlation analysis of data: What it is and how to apply it. (n. d.). Retrieved from https://ua5.org
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
Intelliarts. (2024). Machine learning implementation in business [10 use cases]. Retrieved from https://intelliarts.com/blog/machine-learning-business-applications/
Kaggle. (n. d.). Dataset for predicting consumer purchasing behavior. Retrieved from https://www.kaggle.com/datasets/rabieelkharoua/predict-customer-purchasebehavior-dataset
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Phuoc, T., Anh, P. T. K., Tam, P. H. et al. (2024). Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam. Humanities and Social Sciences Communications, 11, 393.
PixelPlex. (2023). Top 10 machine learning applications in business [2023 list]. Retrieved from https://pixelplex.io/blog/machine-learning-applications-in-business/
ProjectPro. (2024). Stock closing price prediction using machine learning techniques.
Scikit-learn. (n. d. a). Comparing randomized search and grid search for hyperparameter estimation. Retrieved from https://scikit-learn.org/stable/auto_examples/- model_selection/plot_randomized_search.html
Scikit-learn. (n. d. b). Comparing random forests and histogram gradient boosting models. Retrieved from https://scikit-learn.org/stable/auto_examples/ensemble/- plot_forest_hist_grad_boosting_comparison.html
Scikit-learn. (n. d. c). Data preparation. Retrieved from https://scikitlearn.org/ stable/modules/generated/sklearn.model_selection.train_test_split.html
Scikit-learn. (n. d. d). StandardScaler. Retrieved from https://scikitlearn.org/ stable/modules/generated/sklearn.preprocessing.StandardScaler.html
Simplilearn. (2024a). Master guide on machine learning for stock prediction.
Simplilearn. (2024b). Stock market prediction using machine learning in 2024.
Sonkavde, G., Dharrao, D. S., Bongale, A. M. et al. (2023). Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications. International Journal of Financial Studies, 11(3), 94.
Zhao, Y., Li, L., Wang, H. et al. (2021). On the impact of sample duplication in machine-learning-based Android malware detection. ACM Transactions on Software Engineering and Methodology, 30(3), Art. 40. Retrieved from https://chapering.github.io/pubs/tosem21impact.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Applied information systems and technologies in the digital society
This work is licensed under a Creative Commons Attribution 4.0 International License.
