Smart Approach for Real-Time Gender Prediction of European School’s Principal Using Machine Learning

  • Yatish Bathla
  • Chaman VermaEmail author
  • Neerendra Kumar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)


Supervised machine learning is used to solve the binary classification problem on four datasets of European Survey of Schools: Information and Communication Technology (ICT) in Education (known as ESSIE) which is supported by European Union (EU). To predict the gender of the principal based on their response for the ICT questionnaire, the authors applied four supervised machine learning algorithms (sequential minimal optimization (SMO), multilayer perception (ANN), random forest (RF), and logistic regression (LR) on ISCED-1, ISCED-2, ISCED-3A, and ISCED-3B level of schools. The survey was conducted by the European Union in the academic year 2011–2012. The datasets have total 2933 instances\ & 164 attributes considered for the ISCED-1 level, 2914 instances\ & 164 attributes for the ISCED-2 level, 2203 instances\ & 164 attributes for the ISCED-3A level and 1820 instances\ & 164 attributes for the ISCED-3B level. On the one hand, SMO classifier outperformed others at ISCED-3A level and on the other hand, LR outperformed others at ISCED-1, ISCED-2, and ISCED-3B. Further, real-time prediction and automatic process of the datasets are done by introducing the concepts of the web server. The server communicates with the European Union web server and displays the results in the form of web application. This smart approach saves the data process and interaction time of humans as well as represents the processed data of the Weka efficiently.


Supervised machine learning Classification Real time Sensitivity Principal gender prediction 



The authors are thankful to the European Commission to provide ESSIE dataset online. Also, second author’s project is also sponsored by the Hungarian Government and Co-financed by the European Social Fund under the project “Talent Management in Autonomous Vehicle Control Technologies (EFOP-3.6.3-VEKOP-16-2017-00001).”


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Authors and Affiliations

  1. 1.Doctoral School of Applied Informatics and Applied MathematicsÓbuda UniversityBudapestHungary
  2. 2.Department of Media and Educational InformaticsEötvös Loránd UniversityBudapestHungary
  3. 3.Department of Computer Science and ITCentral University of JammuJammuIndia

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