学生成绩预测数据集

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details). P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. Available at: Web link Citation Request: Please include this citation if you plan to use this database: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

本数据集聚焦葡萄牙两所中学的学生学业表现。数据集属性涵盖学生成绩、人口统计学特征、社会背景特征及校内相关特征，数据通过采集学校档案与调查问卷获取。本次提供两类数据集，分别对应两门不同科目的学业表现：数学（Mathematics，简称mat）与葡萄牙语（Portuguese language，简称por）。在[Cortez与Silva，2008]的研究中，这两类数据集被应用于二分类、五级分类及回归任务的建模。
重要说明：目标属性G3与G2、G1存在显著相关性。究其缘由，G3为学年最终成绩（于第三学期出具），而G1与G2分别对应第一、第二学期的成绩。若不借助G2与G1预测G3，预测难度将显著提升，但此类预测的应用价值也更高（详见原论文获取更多细节）。
P. Cortez与A. Silva. 利用数据挖掘（Data Mining）预测中学生学业表现. 收录于A. Brito与J. Teixeira主编，第五届未来商业技术会议（FUBUTEC 2008）论文集，第5-12页，葡萄牙波尔图，2008年4月，EUROSIS出版社，ISBN 978-9077381-39-7. 可获取于：Web link
引用要求：若您计划使用本数据集，请引用如下文献：P. Cortez与A. Silva. 利用数据挖掘预测中学生学业表现. 收录于A. Brito与J. Teixeira主编，第五届未来商业技术会议（FUBUTEC 2008）论文集，第5-12页，葡萄牙波尔图，2008年4月，EUROSIS出版社，ISBN 978-9077381-39-7.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于葡萄牙两所中学的学生成绩预测，包含数学和葡萄牙语两个科目的数据，涵盖成绩、人口统计、社交和学校相关特征。目标变量为期末成绩G3，与前期成绩G1和G2强相关，预测G3时缺乏前期成绩会增加难度，但更具实际应用价值，适用于分类和回归分析任务。