Predicting students’ academic performance using a modified kNN algorithm
Moohanad Jawthari, Veronika Stoffová
Abstract
Abstract The target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-nearest-neighbors algorithm that calculates the distances values of categorical (nominal) variables without encoding them. A student’s academic performance dataset is used for testing the enhanced algorithm. It shows that the proposed algorithm outperforms standard one that needs nominal variables encoding to calculate the distance between the nominal variables. The results show the proposed algorithm preforms 14% better than standard one in accuracy, and it is not sensitive to outliers.