A data set of the attributes of 382 students in secondary education collected from two schools. The goal is to predict the grade in math and Portugese at the end of the third period. See the cited sources for additional information.
Format
382 observations from 13 variables represented as a list consisting
of a binary factor response matrix y with two responses: portugese and
math for the final scores in period three for the respective subjects.
The list also contains x: a sparse feature matrix of class
'dgCMatrix' with the following variables:
- school_ms
student's primary school, 1 for Mousinho da Silveira and 0 for Gabriel Pereira
- sex
sex of student, 1 for male
- age
age of student
- urban
urban (1) or rural (0) home address
- large_family
whether the family size is larger than 3
- cohabitation
whether parents live together
- Medu
mother's level of education (ordered)
- Fedu
fathers's level of education (ordered)
- Mjob_health
whether the mother was employed in health care
- Mjob_other
whether the mother was employed as something other than the specified job roles
- Mjob_services
whether the mother was employed in the service sector
- Mjob_teacher
whether the mother was employed as a teacher
- Fjob_health
whether the father was employed in health care
- Fjob_other
whether the father was employed as something other than the specified job roles
- Fjob_services
whether the father was employed in the service sector
- Fjob_teacher
whether the father was employed as a teacher
- reason_home
school chosen for being close to home
- reason_other
school chosen for another reason
- reason_rep
school chosen for its reputation
- nursery
whether the student attended nursery school
- internet
Pwhether the student has internet access at home
Source
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. http://www3.dsi.uminho.pt/pcortez/student.pdf
Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository http://archive.ics.uci.edu/ml/. Irvine, CA: University of California, School of Information and Computer Science.
Preprocessing
All of the grade-specific predictors were dropped from the data set. (Note that it is not clear from the source why some of these predictors are specific to each grade, such as which parent is the student's guardian.) The categorical variables were dummy-coded. Only the final grades (G3) were kept as dependent variables, whilst the first and second period grades were dropped.