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Project that analyzes student enrollment and performance data to help school board predict whether a particular student would be a good candidate for intervention to help him/her pass the final exam.

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m00nd00r/Student-Intervention

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Supervised Learning

Building a Student Intervention System

Install

This project requires Python 2 and uses the Anaconda Python distribution. If you haven't already please download and install Anaconda.

Instructions:

  1. Clone the repository and navigate to the downloaded folder.

    	git clone https://github.com/m00nd00r/Student-Intervention.git
    	cd Student-Intervention
    
  2. Obtain the necessary Python packages.

    For Mac/OSX/Linux:

    	conda env create -f requirements/intervention-osx.yml
    	source activate intervention
    

    For Windows:

    	conda env create -f requirements/intervention-windows.yml
    	activate intervention
    

Code

Code is in the student_intervention.ipynb notebook file.

Run

In a terminal or command window, navigate to the top-level project directory student_intervention/ (that contains this README) and run the following command:

jupyter notebook student_intervention.ipynb

This will open the Jupyter Notebook software and project file in your browser.

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)

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Project that analyzes student enrollment and performance data to help school board predict whether a particular student would be a good candidate for intervention to help him/her pass the final exam.

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