Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/27400
Title: | Comparative Data Analysis and Visualization of Sales Performance for Flutur Shop in Kosovo and North Macedonia | Authors: | Chiljafi, Majlinda Bajrami, Merxhan Armenski, Goce |
Keywords: | sales data analysis, data visualization, beauty industry, magnetic lashes, Flutur Shop, Kosovo, North Macedo nia, customer preferences, product performance, market trends | Issue Date: | Jul-2023 | Publisher: | Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia | Series/Report no.: | CIIT 2023 papers;25; | Conference: | 20th International Conference on Informatics and Information Technologies - CIIT 2023 | Abstract: | This research paper presents a comprehensive data analysis and visualization of Flutur Shop’s sales performance in Kosovo and North Macedonia. Our objective is to understand the underlying patterns and trends that can provide valuable insights for strategic decision-making and future growth. We have analyzed the data using various descriptive statistics, time series analysis, product analysis, geographic analysis, salesperson analy sis, cancellation analysis, and discount analysis. Our methodology includes data preprocessing, data wrangling, and the utilization of modern data visualization techniques using Python libraries such as pandas, matplotlib, seaborn, and Plotly. By comparing the sales performance across both countries, we have identified key differences and similarities in product preferences, sales trends, and market dynamics. The results of our analysis offer a better understanding of the customer base and market potential in each country, enabling Flutur Shop to optimize its operations, marketing strategies, and product offerings. Furthermore, we discuss the limitations of our study and propose directions for future research to enhance the scope and depth of the analysis. | URI: | http://hdl.handle.net/20.500.12188/27400 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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File | Description | Size | Format | |
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CIIT2023_paper_25.pdf | 9.35 MB | Adobe PDF | View/Open |
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