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UCI-student-performance-mat

UCI-student-performance-mat

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Author: P. Cortez and A. Silva Source: [original](http://archive.ics.uci.edu/ml/datasets/Student+Performance) - 2008 Please cite: 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. 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. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. This dataset regard 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). Attributes 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>1 hour) 14 studytime - weekly study time (numeric: 1 - <2>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) these grades are related with the course subject, Math or Portuguese: 31 G1 - first period grade (numeric: from 0 to 20) 31 G2 - second period grade (numeric: from 0 to 20) 32 G3 - final grade (numeric: from 0 to 20, output target)

33 features

G3 (target)numeric18 unique values
0 missing
schoolstring2 unique values
0 missing
sexstring2 unique values
0 missing
agenumeric8 unique values
0 missing
addressstring2 unique values
0 missing
famsizestring2 unique values
0 missing
Pstatusstring2 unique values
0 missing
Medunumeric5 unique values
0 missing
Fedunumeric5 unique values
0 missing
Mjobstring5 unique values
0 missing
Fjobstring5 unique values
0 missing
reasonstring4 unique values
0 missing
guardianstring3 unique values
0 missing
traveltimenumeric4 unique values
0 missing
studytimenumeric4 unique values
0 missing
failuresnumeric4 unique values
0 missing
schoolsupstring2 unique values
0 missing
famsupstring2 unique values
0 missing
paidstring2 unique values
0 missing
activitiesstring2 unique values
0 missing
nurserystring2 unique values
0 missing
higherstring2 unique values
0 missing
internetstring2 unique values
0 missing
romanticstring2 unique values
0 missing
famrelnumeric5 unique values
0 missing
freetimenumeric5 unique values
0 missing
gooutnumeric5 unique values
0 missing
Dalcnumeric5 unique values
0 missing
Walcnumeric5 unique values
0 missing
healthnumeric5 unique values
0 missing
absencesnumeric34 unique values
0 missing
G1numeric17 unique values
0 missing
G2numeric17 unique values
0 missing

19 properties

395
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
16
Number of numeric attributes.
0
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-4.04
Average class difference between consecutive instances.
48.48
Percentage of numeric attributes.
0.08
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

8 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: G3
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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