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Now You See Me: High School Dropout and Machine Learning

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  • Dario Sansone

    (Georgetown University)

Abstract

In this paper, we create an algorithm to predict which students are eventually going to drop out of US high school using information available in 9th grade. We show that using a naive model - as implemented in many schools - leads to poor predictions. In addition to this, we explain how schools can obtain more precise predictions by exploiting the big data available to them, as well as more sophisticated quantitative techniques. We also compare the performances of econometric techniques like Logistic Regression with Machine Learning tools such as Support Vector Machine, Boosting and LASSO. We offer practical advice on how to apply the new Machine Learning codes available in Stata to the high dimensional datasets available in education. Model parameters are calibrated by taking into account policy goals and budget constraints.

Suggested Citation

  • Dario Sansone, 2017. "Now You See Me: High School Dropout and Machine Learning," 2017 Stata Conference 5, Stata Users Group.
    • Handle: RePEc:boc:scon17:5
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    File URL: http://fmwww.bc.edu/repec/scon2017/Baltimore17_Sansone.pdf
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    Cited by:

    1. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I20 - Health, Education, and Welfare - - Education - - - General

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