Project 2: Supervised Learning
Building a Student_Intervention_Predict_System
运用到AdaBoosting, RandomForest 以及LogisticRegression 三种常用分析算法
最终通过GridSearch 以及 列表比较其性能与预测正确率 选择最佳方案 来构建model
利用如下大量学生的关联信息(包括:个人,学校,性别,年龄,家庭情况,学生行为如缺课,喝酒等)建立一个预测系统
预测这个学生的成绩水平,最终实现有效的预测其 期末考试成绩,
使得学校&老师在学期中途可以根据系统预测结果对每一位学生关注到其学业的变化趋势.
能够尽早对学生教育实现有效监督和提前管理
The GridSearch and listing comparing with others
compare its performance with the accuracy of prediction to select the best scheme to construct model
Associated with a large number of students (including the following information: individual, school, gender, age, family situation, student behavior such as school, drinking etc.) to establish a forecasting system
predicted that the level of student achievement, realize the effective prediction of final exam,
The school teacher
& in the middle of a term according to the system prediction results for each student to pay attention to the trend of education.
as soon as possible to the education of students to achieve effective supervision and management in advance
Data
The dataset used in this project is included as student-data.csv. This dataset has the following attributes:
school: student's school (binary: "GP" or "MS")sex: student's sex (binary: "F" - female or "M" - male)age: student's age (numeric: from 15 to 22)address: student's home address type (binary: "U" - urban or "R" - rural)famsize: family size (binary: "LE3" - less or equal to 3 or "GT3" - greater than 3)Pstatus: parent's cohabitation status (binary: "T" - living together or "A" - apart)Medu: mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)Fedu: father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)Mjob: mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")Fjob: father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")reason: reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")guardian: student's guardian (nominal: "mother", "father" or "other")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)studytime: weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)failures: number of past class failures (numeric: n if 1<=n<3, else 4)schoolsup: extra educational support (binary: yes or no)famsup: family educational support (binary: yes or no)paid: extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)activities: extra-curricular activities (binary: yes or no)nursery: attended nursery school (binary: yes or no)higher: wants to take higher education (binary: yes or no)internet: Internet access at home (binary: yes or no)romantic: with a romantic relationship (binary: yes or no)famrel: quality of family relationships (numeric: from 1 - very bad to 5 - excellent)freetime: free time after school (numeric: from 1 - very low to 5 - very high)goout: going out with friends (numeric: from 1 - very low to 5 - very high)Dalc: workday alcohol consumption (numeric: from 1 - very low to 5 - very high)Walc: weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)health: current health status (numeric: from 1 - very bad to 5 - very good)absences: number of school absences (numeric: from 0 to 93)passed: did the student pass the final exam (binary: yes or no)
Install
This project requires Python 2.7 and the following Python libraries installed: