TY - GEN
T1 - Education and socio economic factors impact on earning for Pakistan - A bigdata analysis
AU - Younas, Neelam
AU - Asghar, Zahid
AU - Qayyum, Muhammad
AU - Khan, Fazlullah
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - CART
KW - Classification and regression tree
KW - Cross validation
KW - Pruning
UR - http://www.scopus.com/inward/record.url?scp=85010032252&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-51207-5_22
DO - 10.1007/978-3-319-51207-5_22
M3 - Conference contribution
AN - SCOPUS:85010032252
SN - 9783319512068
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 215
EP - 223
BT - Future Intelligent Vehicular Technologies - 1st International Conference, Future 5V 2016, Revised Selected Papers
A2 - Ferreira, Joaquim
A2 - Alam, Muhammad
PB - Springer Verlag
T2 - 1st International Conference on Future Intelligent Vehicular Technologies, Future 5V 2016
Y2 - 15 September 2016 through 15 September 2016
ER -