Predicting the admission decision acandidate to the School of Physical Education and Sport at Çukurova University by using different machine learning algorithms

The purpose of this thesis is to develop new prediction to estimate the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University ones he knows his skores from the physical ability test. The classifiers used to evaluate the performance of the prediction models include Support Vector Machine (SVM), Multilayer Perspectron (MLP), Logistic Regression(LR), Radian Basis Function (RBF), Network, Single Desicion Tree(SDT),and K-Means Clustering. Experiments have been condusted on two datesets that include real test result of the candidates who applied to the School in 2006 and 2007, respectively. For model testing and validation ,5-fold and 10-fold cross validation as well as several diffrent percent splits of training/testing date have been used. The performance of the classifiers on the datasets has been evaluated by calculating the classification accuracy and several other performance metrics. The results show that classification accuracy of the SVM classifier using 10-fold cross validation achieves the highest accuracy with 97.90% and 91.45% for the 2006 and 2007 datasets respectively. The rank ing among the six classifiers in terms of achieved classification accuracy has been determined as SVM, LR, MLP, RBF, SDT AND K-Means Clustering. Key Words: Machine Learning, Physical Ability Test, Prediction,Desicion Support Systems.