Future Intelligent Vehicular Technologies. First International Conference, Future 5V 2016, Porto, Portugal, September 15, 2016, Revised Selected Papers

Research Article

Education and Socio Economic Factors Impact on Earning for Pakistan - A Bigdata Analysis

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  • @INPROCEEDINGS{10.1007/978-3-319-51207-5_22,
        author={Neelam Younas and Zahid Asghar and Muhammad Qayyum and Fazlullah Khan},
        title={Education and Socio Economic Factors Impact on Earning for Pakistan - A Bigdata Analysis},
        proceedings={Future Intelligent Vehicular Technologies. First International Conference, Future 5V 2016, Porto, Portugal, September 15, 2016, Revised Selected Papers},
        proceedings_a={FUTURE 5V},
        year={2017},
        month={1},
        keywords={CART Classification and regression tree Pruning Cross validation},
        doi={10.1007/978-3-319-51207-5_22}
    }
    
  • Neelam Younas
    Zahid Asghar
    Muhammad Qayyum
    Fazlullah Khan
    Year: 2017
    Education and Socio Economic Factors Impact on Earning for Pakistan - A Bigdata Analysis
    FUTURE 5V
    Springer
    DOI: 10.1007/978-3-319-51207-5_22
Neelam Younas,*, Zahid Asghar,*, Muhammad Qayyum1,*, Fazlullah Khan2,*
  • 1: Pakistan Institute of Development Econonics
  • 2: Abdul Wali Khan University Mardan
*Contact email: qauidian2006@yahoo.com, g.zahid@gmail.com, qayyum2494@gmail.com, fazlullah@awkum.edu.pk

Abstract

This paper give an insight on effect of education and socio economic factors on education on earning for Pakistan using data mining technique Regression tree and classification tree (CART). Labor force survey data used in this paper. Variables used as predictors in the study are Education, Gender, Status, Training, and Occupation, Location of working, Training, Experience, Age and Type of industry, where monthly income is used as an independent variable. In case of classification income is divided in Quintiles, which is used as a dependent variable for classification variable. Type of industry, education, age and occupation are found significant variables in both classification and regression tree. Regression trees shows that instead of education type of industry is the most important variable and sex and education are the least important variables. Classification tree also shows that Type of industry is the most significant variable which effects the earning of an individual, then age and occupation of an individual come and education is the least important variable where the rest of predictors play no role in earning of an individual.