Student Intervention System

The goal of this project is to predict whether a student is going to be successful in their academics or not. Given a student, we would like to assign the categories pass(1) and not pass(0) to that student, so that teachers and other people could administer early interventions.

To achieve this, a supervised learning model was used to learn how a student’s performance indicators lead to whether a student will graduate or otherwise. The model can do this because we already have existing data on students who have and have not graduated, so the model can learn the performance indicators of those students. Based on the student’s performance indicators, the model would output a weight for each performance indicator.

View the source code here

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)

Methodology

• Two supervised learning models that are appropriate for this problem and available in scikit-learn were chosen.
  • Bagging with K-Nearest Neighbors
  • Suport Vecotr Machines
• Then the model was fit to varying sizes of training data (100 data points, 200 data points, and 300 data points)
• Next, the F1 scores were measured for the training and test set for model performance and evaluation
• Finally, the best model was selected

View the source code here