Prediction of Students' Academic Success Using Data Mining Methods

Success is very important for all of us. Most people wants prosperity, reputation, and richness that can only be achieved with the success. A society that wants to be successful should pay attention to their new generation because they are the future of the world. If we want to invest to our future, we must contribute to success of our new generations. Therefore, in this study, the academic performance of the students that belong to different levels of education like primary, secondary, and high school levels is tried to be determined by applying various classification methods such as Multilayer Perceptron (MLP), Random Forest (RF), Naive Bayes (NB), Decision Tree (J48), and Voting classifiers. It is also observed which characteristics are more related to the improvement of academic performance of the students. Features like absence of student, parent's school satisfaction, raising hands on class, and parent who is responsible for the student can affect the success of the student. A comparison is made with other study that previously worked on the same data set. As a result, better classification accuracy is achieved. We observe the best classification accuracy as 80.6% by Voting classifier, while the previous study has the highest accuracy as 79.1% by applying Artificial Neural Network (ANN) classifier. Also, in our study, Apriori algorithm is applied to detect relationships between features.