Kariyer Planlama İçin Karar Destek Sistemi
Özet
In the last years, the increasing number of artificial intelligence applications have been
continuously invading our daily lives. This thesis, as a result of the introduction of
machine learning approaches to the career planning domain, has been undertaken in order
to develop a recommender system that counsels and proposes a work industry to
university graduates. A system based on machine learning algorithms that recommends
to new graduates an industry to work at, based on the education history, grades and
personal information of previous graduates is designed in this study. The Cross Industry
Standard Process for Data Mining (CRISP-DM), which is one of the most common data
mining processes, is employed after reviewing the characteristics of the problem at hand.
The six steps of CRISP-DM, namely understanding the business, understanding the data,
preparing the data, modelling, evaluation and setting out, have guided the research
methodology. In the modeling phase KNN, Random Forest, Naive Bayes, Support Vector
Machines and Decision Tree machine learning algorithms have been utilized. In order to answer the research questions set by this thesis, a case study based on the data collected
by Hacettepe University Department of Industrial Engineering and Hacettepe University
Student Affairs Office (ÖİDB) has been designed and executed. At the end of the
research, the accuracy of supervised machine learning algorithms has been examined with
the use of a confusion matrix, and the best compared result has been obtained from
Random Forest (with a 67,46% accuracy).
