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Deep Health Alcohol

Information

https://drive.google.com/drive/folders/0BytZsWzpd8xeZHJfSjBmYTRIaDQ

Installation

Python Installation: http://www.pyzo.org/start.html

Python SciKit: http://scikit-learn.org/stable/install.html

R Installation: https://www.rstudio.com/products/rstudio/download/

Attributes for housing data.csv

  1. CRIM: per capita crime rate by town
  2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
  3. INDUS: proportion of non-retail business acres per town
  4. CHAS: Charles River dummy variable 1 if tract bounds river; 0 otherwise)
  5. NOX: nitric oxides concentration (parts per 10 million)
  6. RM: average number of rooms per dwelling
  7. AGE: proportion of owner-occupied units built prior to 1940
  8. DIS: weighted distances to five Boston employment centres
  9. RAD: index of accessibility to radial highways
  10. TAX: full-value property-tax rate per $10,000
  11. PTRATIO: pupil-teacher ratio by town
  12. LSTAT: % lower status of the population
  13. MEDV: Median value of owner-occupied homes in $1000's

Attributes for student-mat-sample.csv:

  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)

Additional note: there are several (382) students that belong to both datasets .

These students can be identified by searching for identical attributes that characterize each student, as shown in the annexed R file.

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