Student Grade Prediction

Student Grade Prediction

A student’s academic performance—shaped by factors like study habits, family background, and school environment — directly impacts their future career prospects, earning potential, and overall well-being. By identifying these influences and applying targeted strategies, educators and policymakers can enhance learning and academic outcomes and better support student in achieving their full potentials and success.

The objective is to equip educational policymakers with predictive tools and actionable insights to enhance student performance and promote academic success

In this project, we will develop a model to predict which students need intervention before they fail their college program, using the UCI Student Performance dataset from two Portuguese schools.

Data

The data for this project is available here, in CSV format, containing 395 rows and 33 columns. The description of the features are :


- Sex : students sex (binary: "F" - female or "M" - male)
- Age : students age (numeric: from 15 to 22)
- School : students school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira) 
- Address : students home address type (binary: "U" - urban or "R" - rural)
- Pstatus: parents cohabitation status (binary: "T" - living together or "A" - apart)
- Medu : mothers education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
- Mjob: mothers job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
- Fedu : fathers education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
- Guardian : students guardian (nominal: "mother", "father" or "other")
- Fjob : fathers job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
- Famsize : family size (binary: "LE3" - less or equal to 3 or "GT3" - greater than 3)
- Famrel : quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
- Reason : reason to choose this school (nominal: close to "home", school "reputation", "course" preference 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).
- Activities : extra-curricular activities (binary: yes or no)
- Paid : extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
- Internet : Internet access at home (binary: yes or no)
- Nursery : attended nursery school (binary: yes or no)
- Higher : wants to take higher education (binary: yes or no).
- Romantic : with a romantic relationship (binary: yes or no)
- 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)
- Walc : weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
- Dalc : workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
- famsup : current health status (numeric: from 1 - very bad to 5 - very good)
- Health : current health status (numeric: from 1 - very bad to 5 - very good)
- Absences : number of school absences (numeric: from 0 to 93)
- G1 : The first period grade
- G2 : The second period grade
- G3 : The final period grade

Here is a link to all the code for this project on Github.