Goal: Evaluate the Performance of Different Supervised Learning Models and Implement Parameter Tuning for a Student Intervention System
This report is a modified version of my solution to the 'Building a Student Intervention System' Udacity Project that is part of the Machine Learning Engineer Nanodegree program
The report is saved in an iPython Notebook format. To review it click on the Student_Intervention.ipynb file.
If you want to run the code in your computer you will need to follow the Install and Run instructions.
The dataset used in this project is provided by Udacity and is included here 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)
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
In a terminal or command window, navigate to the top-level project directory that contains this README and run one of the following commands:
ipython notebook student_intervention.ipynbor
jupyter notebook student_intervention.ipynbThis will open the Jupyter Notebook software and project file in your browser.