By Robert Latimer
The goal of this project is to identify at-risk students in order to stage an early intervention and improve the student's likelihood of graduating. wholesale distributor's different customer clusters by analyzing data pertaining to customer behaviors. By implementing a SVM algorithm, we are able to identify commonly found behaviors or traits that differ between students that graduated and those that failed by essentially drawing a decision boundary between the two groups of students. Educators can use this program to help identify students that are at-risk of failing before the students have followed too deeply upon a negative path.
For full detail of the project, please see 'student_intervention_RLatimer.ipynb'.
This project requires Python 2.7 and the following Python libraries installed:
All original code was completed in the student_intervention_RLatimer.ipynb iPython notebook file.
In a terminal or command window, navigate to the top-level project directory student_intervention/ (that contains this README) and run one of the following commands:
ipython notebook student_intervention_RLatimer.ipynb
jupyter notebook student_intervention_RLatimer.ipynb
This will open the iPython Notebook software and project file in your browser.
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)