Investigating the predictors of Portuguese high school student performance
This project is proudly brought to you by:
In developing social initiatives to improve student scores, it could be immensely useful to understand what attributes play a strong role in predicting student performances on various subjects. Without identifying these key factors, such initiatives would likely be less effective and would fail to provide a high return on the school board or government's investment.
For this project we are trying to answer the question:
What are the top five features that most strongly predict high school student’s performances in their Portuguese language course?
To do that, we are working with a UCI Machine Learning Repository (Cortez 2014) data set, which includes student grades for two subjects (Math and Portuguese), demographic, social and other school related features.
The project is segmented into the various phases:
- Downloading of dataset
- Wrangling and splitting of train versus test set
- Performing exploratory data analysis
- Fit and tune several predictive models with performance comparison, while extracting out the top predictive features from the best model
A detailed analysis and conclusion on the top 5 predictors can be found in our final report.
- Using Docker
note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)
To replicate the analysis, install Docker. Then clone this GitHub repository and run the following command at the command line/terminal from the root directory of this project:
docker run --rm -v "/$(pwd):/home/highschool_portuguese_grades" kfoofw/portuguese_grades make -C /home/highschool_portuguese_grades all
To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:
docker run --rm -v "/$(pwd):/home/highschool_portuguese_grades" kfoofw/portuguese_grades make -C /home/highschool_portuguese_grades clean
- Without using Docker
To replicate the analysis, clone this GitHub repository, install the dependencies listed below, and run the following commands at the command line/terminal from the root directory of this project:
make all
To reset the repo to a clean state, with no intermediate, img, or result files, run the following command at the command line/terminal from the root directory of this project:
make clean
- Python 3.7.3 (Van Rossum and Drake 2009) and Python packages:
- docopt==0.6.2 (Keleshev 2014)
- pytest==5.3.4 (Krekel and team 2015)
- bayesian-optimization==1.01 (Nogueira 2017)
- altair==3.2.0 (VanderPlas et al. 2018)
- pandas==0.25.3 (McKinney and others 2010)
- scikit-learn==0.22.1 (Buitinck et al. 2013)
- numpy==1.17.4 (Oliphant 2006)
- xgboost==0.90 (Chen and Guestrin 2016)
- lightgbm==2.3.0 (Ke et al. 2017)
- selenium==3.141.0
- R version 3.6.1 (R Core Team 2019) and R packages:
- docopt==0.6.1 (de Jonge 2018)
- knitr==1.26 (Xie 2014)
- tidyverse==1.2.1 (Wickham 2017)
- caret==6.0-85 (Kuhn 2020)
- ggridges==0.5.2 (Wilke 2020)
- ggcorrplot==0.1.3 (Kassambara 2019)
- testthat==2.2.1 (Wickham 2011)
- General:
- chromedriver==79.0.3945.130
The Student Performance Predictors materials here are licensed under the Creative Commons Attribution 2.5 Canada License (CC BY 2.5 CA). If re-using/re-mixing please provide attribution and link to this webpage.
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Cortez, Paulo. 2014. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml.
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