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Abstract


INVESTIGATION OF THE MOST PROBLEMS IN FURNITURE PRODUCTS WITH DATA MINING

Furniture has an important place in improving the quality of life of people. For the consumers, the problems experienced in the furniture product are very effective on the purchase intention. Data mining is used effectively to solve different problems in the world of science and industry. At the same time, data science offers scientists many ways to find hidden information. In this research, data mining was used to analyze the problems experienced by consumers in furniture products. For this purpose, data were obtained by the survey method. In the questionnaire, the consumers' information, the most common problems in furniture products and the furniture types with the most problems were asked. A multivariate analysis approach was applied to analyze the data, using the Rapidminer software and FP-Growth algorithm. As a result of the study, it was found that men with single marital status and university education complained about the comfort of furniture with a reliability of 94%. At the same time, it is the women who complain that the marital status is married with a 100% reliability rate, between the ages of 18-30 and the furniture did not last enough. As a result of this study, it was determined that consumer satisfaction can be increased by producing furniture based on data science.



Keywords
Data Mining, Furniture, Problems, FP-Growth Algorithm.



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