Skip to content

Investigated the factors that affect a student's performance in high school. Trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Selected the best model based on relative accuracy and efficiency.

Notifications You must be signed in to change notification settings

adityasiwan/Student-Intervention-System

Repository files navigation

##Building a Student Intervention System

Investigated the factors that affect a student's performance in high school. Trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Selected the best model based on relative accuracy and efficiency.

Install

This project requires Python 2.7 and the following Python libraries installed:

Data

The dataset used in this project is included as student-data.csv. This dataset has the following attributes:

  • school ? student's school (binary: "GP" or "MS")
  • 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)
  • romantic ? with a romantic relationship (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)
  • Dalc ? workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
  • Walc ? weekend alcohol consumption (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)
  • passed ? did the student pass the final exam (binary: yes or no)

About

Investigated the factors that affect a student's performance in high school. Trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Selected the best model based on relative accuracy and efficiency.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published