ABSTRACT
Today decision-making and business planning are fundamental challenges, especially for retailers. Vendors are paying more attention to find out the age group of customers most affected by and interested in special offers on events such as Blessed Friday (falsely known as Black Friday). Besides, they want to know which strategy or marketing plan should be adopted to grasp more customers. Therefore, to assess customers’ needs or figure out the group more interested in different Blessed Friday deals is challenging. Many solutions to this problem, like analysis of Blessed Friday consumer behaviour, classification of retail stores, etc., have been proposed. Still, such solutions do not consider the age group of customers. In this research, a comparison of techniques for predicting the age group of shoppers on a Blessed Friday is proposed based on machine learning. This work is done on a dataset obtained from a repository published online. During pre-processing of the dataset, missing values are replaced. Algorithms such as Decision Tree, K-Nearest Neighbor, Naïve Bayes, and Neural Network are applied to determine the most suitable algorithm. This study provides a good understanding of classification, targets the age group more interested in Blessed Friday, and provides primary research ground for further analysis in this domain.
- L. Simpson, L. Taylor, K. O'Rourke, and K. Shaw, "An analysis of consumer behavior on Black Friday," American International Journal of Contemporary Research, 2011.Google Scholar
- E. Swilley and R. E. Goldsmith, "Black Friday and Cyber Monday: Understanding consumer intentions on two major shopping days," Journal of retailing and consumer services, vol. 20, pp. 43-50, 2013.Google ScholarCross Ref
- M. Bilal, H. Israr, M. Shahid, and A. Khan, "Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques," Journal of King Saud University-Computer and Information Sciences, vol. 28, pp. 330-344, 2016.Google ScholarDigital Library
- F. Ellis-Chadwick, N. Doherty, and C. Hart, "Retailer adoption of the Internet – Implications for retail marketing," European Journal of Marketing, vol. 34, 09/01 2000.Google Scholar
- S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, "Data preprocessing for supervised leaning," International Journal of Computer Science, vol. 1, pp. 111-117, 2006.Google Scholar
- M.-L. Zhang and Z.-H. Zhou, "A k-nearest neighbor based algorithm for multi-label classification," in 2005 IEEE international conference on granular computing, 2005, pp. 718-721.Google Scholar
- A. Jovic, K. Brkic, and N. Bogunovic, "An overview of free software tools for general data mining," in 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014, pp. 1112-1117.Google Scholar
- K. Rangra and K. Bansal, "Comparative study of data mining tools," International journal of advanced research in computer science and software engineering, vol. 4, pp. 216-223, 2014.Google Scholar
- Y. Ramamohan, K. Vasantharao, C. K. Chakravarti, and A. Ratnam, "A study of data mining tools in knowledge discovery process," International Journal of Soft Computing and Engineering (IJSCE) ISSN, vol. 2, pp. 2231-2307, 2012.Google Scholar
- F. Kamiran and T. Calders, "Data preprocessing techniques for classification without discrimination," Knowledge and Information Systems, vol. 33, pp. 1-33, 2012.Google ScholarDigital Library
- K. Suresh, "An overview of randomization techniques: an unbiased assessment of outcome in clinical research," Journal of human reproductive sciences, vol. 4, p. 8, 2011.Google ScholarCross Ref
- T. M. Alam and M. J. Awan, "Domain Analysis of Information Extraction Techniques," International Journal of Multidisciplinary Sciences and Engineering, vol. 9, pp. 1-9.Google Scholar
- Y. Ali, A. Farooq, T. M. Alam, M. S. Farooq, M. J. Awan, and T. I. Baig, "Detection of Schistosomiasis Factors Using Association Rule Mining," IEEE Access, vol. 7, pp. 186108-186114, 2019.Google ScholarCross Ref
- M. U. Ghani, T. M. Alam, and F. H. Jaskani, "Comparison of Classification Models for Early Prediction of Breast Cancer," in 2019 International Conference on Innovative Computing (ICIC), 2019, pp. 1-6.Google Scholar
- T. I. Baig, T. M. Alam, T. Anjum, S. Naseer, A. Wahab, M. Imtiaz, , "Classification of Human Face: Asian and Non-Asian People," in 2019 International Conference on Innovative Computing (ICIC), 2019, pp. 1-6.Google Scholar
- M. Z. Latif, K. Shaukat, S. Luo, I. A. Hameed, F. Iqbal, and T. M. Alam, "Risk Factors Identification of Malignant Mesothelioma: A Data Mining Based Approach," in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020, pp. 1-6.Google Scholar
- S. Kamran, I. Farhat, A. Talha Mahboob, A. Gagandeep Kaur, D. Liton, K. Abdul Ghaffar, , "The Impact of Artificial intelligence and Robotics on the Future Employment Opportunities," Trends in Computer Science and Information Technology, vol. 5, p. 5, 2020.Google Scholar
- T. M. Alam, K. Shaukat, M. Mushtaq, Y. Ali, M. Khushi, S. Luo, , "Corporate Bankruptcy Prediction: An Approach Towards Better Corporate World," The Computer Journal, 2020.Google ScholarCross Ref
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