Student Alcoholism and Academic Performance Dataset

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Overview

This dataset provides comprehensive insights into the socioeducational factors influencing student behavior, with a focus on alcohol consumption and academic performance in high school. Collected through a survey conducted among high school students, the dataset includes various social and gender-related information, as well as grades in the Portuguese Language discipline.

Source

The data was gathered by P. Cortez and A. Silva for the study “Using Data Mining to Predict High School Student Performance,” presented at the 5th Future Business Technology Conference (FUBUTEC, 2008) in Porto, Portugal.

Use Cases

This dataset is valuable for exploring the complex interplay between social and educational factors on student behavior, focusing on alcohol consumption patterns and academic performance. Researchers, educators, and data enthusiasts can leverage this dataset for various analyses and predictive modeling.

Citation

Please cite the original source if you use or refer to this dataset:

P. Cortez e A. Silva. Usando a Mineração de Dados para Prever o Desempenho do Aluno do Ensino Médio. Em A. Brito e J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, abril de 2008, EUROSIS, ISBN 978-9077381-39-7.

Or if you can't find the author, you can cite this dataset by the DOI present in it

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Features

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 age (numeric: 15 to 22)
4. address: Type of student's residential address (binary: 'U' - urban or 'R' - rural)
5. famsize: Family size (binary: 'LE3' - less than or equal to 3 or 'GT3' - greater than 3)
6. Pstatus: Parents' cohabitation status (binary: 'T' - living together or 'A' - separated)
7. Medu: Mother's education level (0 - none, 1 - Elementary School 1, 2 - Elementary School 2, 3 - High School or 4 - Higher Education)
8. Fedu: Father's education level (0 - none, 1 - Elementary School 1, 2 - Elementary School 2, 3 - High School or 4 - Higher Education)
9. Mjob: Mother's job (nominal: teacher, health, services, at_home or Other)
10. Fjob: Father's job (nominal: teacher, health, services, at_home or Other)
11. reason: Reason for choosing this school (nominal: home, reputation, course or other)
12. guardian: Student's guardian (nominal: mother, father or other)
13. traveltime: Travel time from home to school (time intervals: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour or 4 - >1 hour )
14. studytime: Weekly study time (time intervals: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours or 4 - >10 hours)
15. schoolsup: Extra educational support (binary: yes or no)
16. famsup: Family educational support (binary: yes or no)
17. paid: Private classes on subjects related to the course (binary: yes or no)
18. activities: Performs extracurricular activities (binary: yes or no)
19. nursery: Attended daycare (binary: yes or no)
20. higher: Desire to pursue a degree (binary: yes or no)
21. internet: Internet access at home (binary: yes or no)
22. romantic: Are you in a romantic relationship (binary: yes or no)
23. famrel: Quality of family relationships (categorical: from 1 - very bad to 5 - excellent)
24. freetime: Free time after school (categorical: from 1 - very low to 5 - very high)
25. goout: Time with friends (categorical: from 1 - very low to 5 - very high)
26. Dalc: Alcohol consumption on the work day (categorical: from 1 - very low to 5 - very high)
27. Walc: Alcohol consumption on the weekend (categorical: from 1 - very low to 5 - very high)
28. health: Current health status (categorical: from 1 - very bad to 5 - very good)
29. absences: Number of school absences (numeric: from 0 to 93)
30. G1: First semester grade (numeric: from 0 to 20)
31. G2: Second semester grade (numeric: from 0 to 20)

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explorer = pd.read_csv('https://raw.githubusercontent.com/gabrielluizone/FirstCode/main/ipor_explorer.csv')
classification = pd.read_csv('https://raw.githubusercontent.com/gabrielluizone/FirstCode/main/ipor_classification.csv')