Big Data Analysis of College Entrance Examination Candidates' Volunteer Selection Based on Python
Abstract
Purpose – This article adopts a Python crawler technology approach to write crawler program code, extract publicly available college entrance examination volunteer filling data on the internet, and clean and store the data. Then, through analysis of the data, we mainly conducted big data analysis on the indicators used by college entrance examination candidates for voluntary reporting, identify popular majors for college entrance examination candidates to fill in, and providing references for students who are about to participate in the college entrance examination.
Proposed Method – The study will use crawler technology to collect, save, clean, and analyze the data, and finally use data analysis technology to complete data analysis and visual chart display.
Conclusion – The collected data was analyzed and visualized based on the top 10 majors selected for the first batch of undergraduate college entrance examination in Henan Province in 2019, the top 10 majors selected for the second batch of undergraduate college entrance examination, the top 10 majors selected for vocational colleges, the top 10 majors selected for liberal arts, and the top 10 majors selected for science. The analyzed results provide the necessary support for universities to carry out corresponding majors and can also provide a reference for students who are about to participate in the college entrance examination, which is of great significance.
Recommendations – This paper suggests using convenient and efficient Python crawler technology to extract data, store and analyze it, and ultimately display it.
Practical Implication – The analyzed results provide the necessary support for universities to carry out corresponding majors and can also provide a reference for students who are about to participate in the college entrance examination, which is of great significance.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.