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Abstract

The consumer spectrum consists of a wide range, including the affluent, middle-income, and low-income. This consumer shows different behaviors or motivations towards choosing clothes. We want to develop a framework for a Sale Recommendation System. These expert System can be helpful for sale persons, fashion designer, promoter, brand manager as well as sponsor of Recommendation System. The study implemented the Data Science approach and techniques to see how reliable Recommendation Systems are and in our selected dataset we have applied different modelling techniques such as KNN, SVM, Bayes Naïve and Decision Tree and fond the NB as the most suitable and practical method of modelling in regard to the accuracy, recall and runtime.

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Correspondence to Haitham M. Alzoubi .

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Appendix [Python Code with Outputs]

Appendix [Python Code with Outputs]

A program code to import dress data of Pandas that include style, price, rating, size, season, neckline, sleeve length, waistline, material, decoration, and pattern type.
A text document of values for style, price, rating, season, neckline, sleeve length, waistline, material, decoration, pattern type, and the recommendation for count, unique, top, and frequency.
2 tables. Table 1 has 2 rows and table 2 has 500 rows and 12 columns. The column headers are style, price, rating, season, size, neckline, sleeve length, waistline, material, decoration, pattern type, and recommendation.

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Salahat, M., Al-Dmour, N.A., Said, R.A., Alzoubi, H.M., Alshurideh, M. (2023). Development of Data Mining Expert System Using Naïve Bayes. In: Alshurideh, M., Al Kurdi , B.H., Masa’deh, R., Alzoubi , H.M., Salloum, S. (eds) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-031-12382-5_134

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