😊 Data Science and Data Visualization Project 💯

Topic: How ambient factors affect student's performance


STORY & IDEA

The grading system was always the key component utilized to measure the pupils' level, competence, or even IQ throughout our schooling process. However, does a student's grade accurately reflect their ability or intelligence? We'd say it does occasionally, but not most of the time.

Our project's goal is to demonstrate that assessing a student only on his or her grade is incorrect. We will visualize the data to highlight the association between a student's grade and their background characteristics such as family, parent, age, and many more. Hopefully, the audience will be able to detect a significant association between these environmental elements and the student's academic achievement as a result of our depiction.

The ultimate goal is to convey the notion that assessing a student only on his or her grade is completely wrong. Because a student's grade may be heavily influenced by their history, situation, and a variety of other environmental factors.


Data Sources

Attributes in my data

Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:

  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)