This research attempts to understand the underlying factors influencing the success of engineering education. In doing so, it analyzes the available data regarding the non-mandatory positioning test, which was introduced at the Belgian universities for engineering programs in summer 2013. Predictive model learning algorithms are used to make prediction for unseen data. In this research Naïve Bayes based algorithm is used to predict the contributing factors for the success in engineering education. The result shows that the prior academic achievement (choosing option of higher hours in mathematics, percentage of marks in mathematics in high school) of the students influences the score of the test. It also shows that the score of the test along with prior mathematical experience is a good predictor for the success and failure of students in engineering education. Moreover, the research finds that the test score has a high predictive power for the result of engineering study especially for the students who are more likely to do badly. On the other hand, the results indicate that gender is not an obstacle in study success in engineering education. This study finds that the girls who do conduct higher hours of mathematics in their high school and go for the engineering study do equally well as boys.