student: Student grade prediction
In Rkabacoff/qacr: Functions to Facilitate Data Science Projects

Description Usage Format Details Note Source Examples

Student math achievement in secondary education of two Portuguese schools

student

A data frame with 395 rows and 33 variables:

school: factor. student's school ('Gabriel Pereira' or 'Mousinho da Silveira').
sex: factor. student's sex ('Female' or 'Male').
age: integer. student's age (numeric: from 15 to 22).
address: factor. student's home address type ('Urban' or 'Rural').
famsize: factor. family size (binary: 'Less or equal to 3' or 'Greater than 3').
Pstatus: factor. parent's cohabitation status ('Living together' or 'Living apart').
Medu: ordered factor. mother's education ('primary education (4th grade)', '5th to 9th grade', 'secondary education' or 'higher education').
Fedu: ordered factor. father's education ('primary education (4th grade)', '5th to 9th grade', 'secondary education' or 'higher education').
Mjob: factor. mother's job ('teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other').
Fjob: factor. father's job ('teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other').
reason: factor. reason to choose this school (close to 'home', school 'reputation', 'course' preference or 'other').
guardian: factor. student's guardian ('mother', 'father' or 'other').
traveltime: ordered factor. home to school travel time ('1 - <15 min.', '15 to 30 min.', 30 min. to 1 hour', or '4 - >1 hour').
studytime: ordered factor. weekly study time ('<2 hours', '2 to 5 hours', '5 to 10 hours', or '>10 hours').
failures: integer. number of past class failures (numeric: n if 1<=n<3, else 4).
schoolsup: factor. extra educational support ('yes' or 'no').
famsup: factor. family educational support ('yes' or 'no').
paid: factor. extra paid classes within the course subject (Math) ('yes' or 'no').
activities: factor. extra-curricular activities ('yes' or 'no').
nursery: factor. attended nursery school ('yes' or 'no').
higher: factor. wants to take higher education ('yes' or 'no').
internet: factor. Internet access at home ('yes' or 'no').
romantic: factor. with a romantic relationship ('yes' or 'no').
famrel: integer. quality of family relationships (from 1 - very bad to 5 - excellent).
freetime: integer. free time after school (from 1 - very low to 5 - very high).
goout: integer. going out with friends (from 1 - very low to 5 - very high).
Dalc: integer. workday alcohol consumption (1 - very low to 5 - very high).
Walc: integer. weekend alcohol consumption (from 1 - very low to 5 - very high).
health: integer. current health status (from 1 - very bad to 5 - very good).
absences: integer. number of school absences (from 0 to 93).
G1: integer. first period grade (from 0 to 20).
G2: integer. second period grade (from 0 to 20).
G3: integer. final grade (from 0 to 20, output target).

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. This dataset provides regarding the performance in Mathematics. 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).

While the original data source provides grades in both Mathematics and Portuguese, this dataset is bases on math scores alone.

Data obtained from UCI Machine Learning Repository.

Original source: Paulo Cortez, University of Minho, GuimarÃ£es, Portugal, http://www3.dsi.uminho.pt/pcortez

Please include this citations when using the data: 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. Web Link.