The dataset contains students last three year exam results combined with other data such as their sex, family size, father and mother job and some of their personal and social life properties. The goal is to train ML models to predict student performance in their last year exams.
Attribute Description
sex student’s sex (binary: female or male)
age student’s age (numeric: from 15 to 22)
school student’s school (binary: Gabriel Pereira or Mousinho da Silveira)
address student’s home address type (binary: urban or rural)
Pstatus parent’s cohabitation status (binary: living together or apart)
Medu mother’s education (numeric: from 0 to 4a)
Mjob mother’s job (nominalb)
Fedu father’s education (numeric: from 0 to 4a)
Fjob father’s job (nominalb)
guardian student’s guardian (nominal: mother, father or other)
famsize family size (binary: ≤ 3 or > 3)
famrel quality of family relationships (numeric: from 1 – very bad to 5 – excellent)
reason reason to choose this school (nominal: close to home, school reputation, course preference 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 school support (binary: yes or no)
famsup family educational support (binary: yes or no)
activities extra-curricular activities (binary: yes or no)
paidclass extra paid classes (binary: yes or no)
internet Internet access at home (binary: yes or no)
nursery attended nursery school (binary: yes or no)
higher wants to take higher education (binary: yes or no)
romantic with a romantic relationship (binary: yes or no)
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)
Walc weekend alcohol consumption (numeric: from 1 – very low to 5 – very high)
Dalc workday 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)
G1 first period grade (numeric: from 0 to 20)
G2 second period grade (numeric: from 0 to 20)
G3 final grade (numeric: from 0 to 20)
a 0 – none, 1 – primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education.
b teacher, health care related, civil services (e.g. administrative or police), at home or other.
Citation: 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