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Final-Project-JCDS05

Tugas akhir Data Science JCDS05

Dataset Background

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. 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).

Dataset source: https://archive.ics.uci.edu/ml/datasets/Student+Performance

Objective

The objective of this project is to predict which features (or attributes) that may have impactful meaning towards predicting grade of a student. These features are collected from demographic, social and school by using school reports and questionnaires. Acknowledging the important features that can affect student's grade or performance enable us to know in which sectors that can be improved or neglected towards making a better educational system.

Features Information

  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 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
  14. studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >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:

  1. G1 - first period grade (numeric: from 0 to 20)
  2. G2 - second period grade (numeric: from 0 to 20)
  3. G3 - final grade (numeric: from 0 to 20, output target)

Homepage

Result page

Based on the dataset and correlation between the student's grades and other attributes (features), there are only few features that has good correlation with the student grades (+- 0.1) There are positive and negative correlation when comparing with the average of student score (AvgGrade). Positive correlation features such as:

  • parent's education and their job (Medu,Fedu, Mjob, Fjob)
  • student's enthusiasm to proceed higher degree of studying (higher)
  • the amount of student spending their time for studying (studytime)
  • the district area of where the student lives (address)

conversely, negative correlation features such as:

  • The number of student previously failed their test (failure)
  • The amount of alcohol consume weekly (Dalc)
  • The number of absence that the student missed (absences)
  • The travel time between the student's house to school

These features are the input of the Machine Learning. Standard Vector Machine (SVM) Algorithm is used for this project to predict whether the student will fail or pass their future exam if they have the attibutes that they inseted.