Saleem, Qudsia2018-01-152018-01-152017https://escholar.umt.edu.pk/handle/123456789/2458Supervised by: Dr. Malik Tahir HassanThe development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). Educational Data mining is a wide-ranging subject and research field which is helping us in exceptional data analysis of educational field. The collected data is shaped as a data set and then various data mining techniques are applied to derive interesting patterns; that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole education system. In this thesis, we explore Educational Data Mining (EDM) and various techniques of EDM mainly classification and outlier analysis which can help us in analyzing and predicting the aspects affecting the students’ as well as institutes’ performance from different dimensions.enEducational data miningVocational education and trainingMS ThesisThesis