数据集.zip

现有某国外高校的高等数学成绩数据集，其中有30个特征，3个成绩值，说明如下： 特征： 1 school - student's school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira) 2 sex - student's sex (binary: "F" - female or "M" - male) 3 age - student's age (numeric: from 15 to 22) 4 address - student's home address type (binary: "U" - urban or "R" - rural) 5 famsize - family size (binary: "LE3" - less or equal to 3 or "GT3" - greater than 3) 6 Pstatus - parent's cohabitation status (binary: "T" - living together or "A" - apart) 7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) 8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) 9 Mjob - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other") 10 Fjob - father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other") 11 reason - reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other") 12 guardian - student's guardian (nominal: "mother", "father" or "other") 13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour) 14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours) 15 failures - number of past class failures (numeric: n if 1<=n<3, else 4) 16 schoolsup - extra educational support (binary: yes or no) 17 famsup - family educational support (binary: yes or no) 18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) 19 activities - extra-curricular activities (binary: yes or no) 20 nursery - attended nursery school (binary: yes or no) 21 higher - wants to take higher education (binary: yes or no) 22 internet - Internet access at home (binary: yes or no) 23 romantic - with a romantic relationship (binary: yes or no) 24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent) 25 freetime - free time after school (numeric: from 1 - very low to 5 - very high) 26 goout - going out with friends (numeric: from 1 - very low to 5 - very high) 27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high) 28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) 29 health - current health status (numeric: from 1 - very bad to 5 - very good) 30 absences - number of school absences (numeric: from 0 to 93) 成绩值： 31 G1 - 第一阶段成绩 (numeric: from 0 to 20) 31 G2 - 第二阶段成绩 (numeric: from 0 to 20) 32 G3 - 最终成绩 (numeric: from 0 to 20, output target) 任务： 请仔细分析该数据集，建立一个适当的模型，预测最终成绩。 要求： 1、用自己熟悉的方法建立模型并独立编程实现这个任务。 2、模型训练好了需要可视化。 3、要输出训练好的模型的参数。 4、请用熟知的评价方法对你训练好的模型进行评价。 5、如有发现完全雷同代码，直接计0分。 6、请直接将相应过程按照要求填写到“《机器学习算法与应用》大作业”文件中