Markpredictor
With the growth of online learning platforms, educational data analytics and prediction has emerged as a promising study area that can aid in the creation of personalised learning systems. The use of Machine Learning to forecast academic performance in students aids future decision-making in the search for techniques that promote continual progress in student learning. The goal is not only to anticipate students' future performance, but also to find the best technique for identifying the most essential features that will be employed in the teacher's performance, as well as the student's motivation, which will ultimately enhance their grades. The most essential reference point for comparing the quality of pupils is their academic performance and application skills.
Attribute Information:
school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
sex - student's sex (binary: 'F' - female or 'M' - male)
age - student's age (numeric: from 15 to 22)
address - student's home address type (binary: 'U' - urban or 'R' - rural)
famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
guardian - student's guardian (nominal: 'mother', 'father' or 'other')
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)
studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
failures - number of past class failures (numeric: n if 1<=n<3, else 4)
schoolsup - extra educational support (binary: yes or no)
famsup - family educational support (binary: yes or no)
paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
activities - extra-curricular activities (binary: yes or no)
nursery - attended nursery school (binary: yes or no)
higher - wants to take higher education (binary: yes or no)
internet - Internet access at home (binary: yes or no)
famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
freetime - free time after school (numeric: from 1 - very low to 5 - very high)
goout - going out with friends (numeric: from 1 - very low to 5 - very high)
health - current health status (numeric: from 1 - very bad to 5 - very good)
absences - number of school absences (numeric: from 0 to 93)\
These grades are related with the course subject, Math or Portuguese:
G1 - first period grade (numeric: from 0 to 20)
G2 - second period grade (numeric: from 0 to 20)
G3 - final grade (numeric: from 0 to 20, output target)\
