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PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Introduction

While harmful and underage drinking are significant public health problems, and they exact an enormous toll on the intellectual and social lives of students. With the uptrend of student having drinking habits, many other problems arise as well, namely the number of crimes offended by students, poor academic performance, as well as relationship issues with family and friends. This problem had been significant over the past few years and soon it will become a social problems. In this study, we try to find out how alcohol consumption affect students general well-being, look for insights that might explain how alcohol affecting the students and finally, what are the countermeasure we can do.

Objective

The aim of this project is to visually analysis how alcohol affect students general well-being, the area we are going to specify are:

• Demographics of student alcohol consumption
• How alcohol affects their academic standing
• Relationship with family

Data Source

We have obtained the 2014 from Kaggle, which can be download from https://www.kaggle.com/uciml/student-alcohol-consumption

The information provided are very extensive and comprehensive, it includes
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)

Technical Complexity

D3.js
In order to create dynamic and interactive visualization, team decides to leverage on the abundant javascript libaries provided by D3.js. However, there are some challenges that team are faced with.

• Unfamiliar with D3.js libraries: In order to select the libraries that satisfies the team's needs, it's necessary to understand the D3 libraries.
• Lack of knowledge on building D3 application: team have no prior experience regarding building application with D3. Hence, there will be a learning curve for the team to explore the development process to build D3 application.

Data Cleaning and data Transformation

• Understand data and its metadata
• Data cleaning and transforming data into required format

Tools/Technologies

• Excel
• Tableau
• D3.js