Authors :
Hrishikesh Bihani, Omkar Dhuri, Chirag Ashar, Uday Rote.
Volume/Issue :
Volume 3 - 2018, Issue 3 - March
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://goo.gl/mjTC46
Thomson Reuters ResearcherID :
https://goo.gl/3bkzwv
Abstract :
This paper recognizes the theory and practice of regression techniques for prediction of technical domain trends by using a transformed data set in ordinal data format. The data formats in technical proficiency and levels provide a process for computation of technical domain trends. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of technical domain trends. The outcomes of both processes are examined and appraised. The primary design is based on regression analysis from WEKA machine learning software. The technical domain trends from Alumni Portal, KJSIEIT is used as our research setting. The data sources are alumni technical proficiency which included skill set, recently used skill and level. The variables included in the data set were formed based on technical domain trends from the alumni profiles. Classifiers in WEKA were used as algorithms to produce the outcomes. This study showed that the outcomes of regression techniques can be improved for the prediction of technical skills which would be trending in the future by using a dataset in standardized ordinal data format.
Keywords :
regression techniques; ordinal data type; machine learning; fundamental analysis; alumni portal; linear regression.
This paper recognizes the theory and practice of regression techniques for prediction of technical domain trends by using a transformed data set in ordinal data format. The data formats in technical proficiency and levels provide a process for computation of technical domain trends. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of technical domain trends. The outcomes of both processes are examined and appraised. The primary design is based on regression analysis from WEKA machine learning software. The technical domain trends from Alumni Portal, KJSIEIT is used as our research setting. The data sources are alumni technical proficiency which included skill set, recently used skill and level. The variables included in the data set were formed based on technical domain trends from the alumni profiles. Classifiers in WEKA were used as algorithms to produce the outcomes. This study showed that the outcomes of regression techniques can be improved for the prediction of technical skills which would be trending in the future by using a dataset in standardized ordinal data format.
Keywords :
regression techniques; ordinal data type; machine learning; fundamental analysis; alumni portal; linear regression.
