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Analyses of Students’ Vocational Data Using Some Selected Classification Algorithms

Unemployment is a major issue battling the Nigerian economy. Today‘s trend is shifting towards skills acquisition rather than on certificate qualification which are capable of making the youths self-dependent, job creators and not job seekers (Okolocha,2012). The management of The Polytechnic, Ibadan, Nigeria is working tirelessly to ensure that each student learns a vocational skill to prepare them for entry into the labour market (Adebayo, 2016). In this study, data mining techniques were used to analyze students’ vocational data of The Polytechnic, Ibadan in Nigeria to discover patterns and relationships that will help the school management to make important decisions and plan better for more productive execution of their vocational skills programme. Experiments were performed using ID3, C4.5 and Naïve Bayes classification algorithms to build models under WEKA 3.8.2 environment. Holdout method and 10 folds cross-validation method were used to test the models. Confusion matrix was used to evaluate the performance of the models on the basis of accuracy, sensitivity and time taken to build the models and it was discovered the C4.5 algorithm gave the best performance with both validation techniques.