Using Python to Implement the Data Analysis for Dropout Rates and Course Performances of Multiple-Enrolled Students

• Main Authors: WU, ZIH-HAO, 吳梓豪
• Other Authors: CHIU, HUNG-PIN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/b9gd94

碩士 === 南華大學 === 資訊管理學系 === 107 === 　　In view of the impact of the domestic minority, college students in D.C. 2006 to 2016 years showed negative growth, enrollment increasingly competition, if the loss of students in school, the school is a big help. This study uses the data mining technology to find out the factors such as the loss rate of the students and the academic performance of the students, and provide relevant suggestions to reduce the loss of students. 　　In this study, a total of 76 valid data were obtained. In the analysis of the data, it was found that the academic performance of the students was still recommended by the stars. The academic performance of the students was still the most prominent part of the students. The academic performance of the students was still the weakest. So academic performance and student enrollment. In the part of the student's wastage, related to the area of residence and admission. In the data mining part of the discovery, to academic total score for the most important factor, school enrollment and residential areas as the second factor, followed by gender and class.