Author(s)

Shuaihang Zhou ¹, Xiangzhen He ¹, Fucheng Wan ², Dongchang Liu ³, Yuguang Wang ¹

Affiliation(s)

¹ Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, 730000, China
² Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province, Northwest Minzu University, Lanzhou, Gansu, 730000, China
³ Yilan Big Data Technology Co., Ltd, Lanzhou, Gansu, 730000, China

Corresponding Author

Xiangzhen He

ABSTRACT

Lanzhou beef noodle is well known, but there is no data scientific analysis of Lanzhou beef noodle related industry research. The linear regression model has significant statistical significance and is widely used in management and economics. As a basic and widely used machine learning algorithm, linear regression plays an important role in exploring the relationship between data in different dimensions. The training of the model usually depends on a large amount of data, which can be obtained through the web crawler. This paper collected data of Lanzhou beef noodle shops through web crawler, cleaned beef noodle information and normalized data after data preprocessing, and analyzed the relationship between the number of shops and the number of population and the number of shops and the number of population density in each province by using linear regression algorithm. Finally, the descriptive statistical results and regression analysis results of "the first side of China" -- Lanzhou beef noodle industry development in the country are visualized through data visualization technology. It provides countermeasures and references for the innovation and development of Lanzhou beef noodle industry employees under the background of big data.

KEYWORDS

Linear Regression, Lanzhou Beef Noodles, Web Crawler, Data Preprocessing, Visualization Analysis

CITE THIS PAPER

Shuaihang Zhou, Xiangzhen He, Fucheng Wan, Dongchang Liu, Yuguang Wang, Analysis of Lanzhou Beef Noodle Industry Based on Linear Regression. Industrial Engineering and Innovation Management (2023) Vol. 6: 66-75. DOI: http://dx.doi.org/10.23977/ieim.2023.060310.

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