# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 3 Methodology
### Data Preparation
The feature selection methods can be divided into supervised and unsupervised. The features in supervised feature selection methods are chosen based on their relationship to the class label. It chooses qualities that are most relevant to the class label. On the other hand, unsupervised feature selection approaches assess feature relevance by exploring data using an unsupervised learning method [38]. The attributes in the dataset will go through the supervised feature selection process using random forest, extra tree, info gain, and chi-square techniques. After the execution, the attributes have been allocated weights based on their relative importance and are sorted in order. For this experiment, drop-out status has been selected as the class label. The selected attributes which were found to be significant are the place of birth, income groups, secondary school, number of activities, CGPA, employment status, registration age, entry qualification, sponsorship, university, the field of study, and drop-out status. Then, [PERSON]'s W statistic is used to assess agreements between all the raters by showing the statistical value between 0 and 1. If the value is \"zero\", there is no agreement between the raters, while the value \"one\" indicates complete agreement. [PERSON]'s W assessment produced a score of 0.8862 and showed good and strong agreement among all raters used. For unsupervised feature selection, a variance threshold will be used on the dataset. Variance is a metric that measures how dispersed the data distribution is within a dataset. Selecting attributes with considerable variance is necessary to avoid developing a biased clustering model and skewed toward certain attributes. Before developing an unsupervised machine learning model, choosing attributes based on their variance is essential. A high variance indicates that the attribute's value is unique or has a high cardinality. Attributes with low variation have relatively comparable values, but attributes with zero variance have similar values. Furthermore, low-variance attributes are close to the mean value, providing minimal clustering information [8]. Because it solely evaluates the input attribute (_x_) without considering data from the dependant attribute, the variance thresholding technique is appropriate for unsupervised modelling (_y_). The variance of all student attributes is shown in Figure 2. As suggested by [39], in this study, the variance threshold value for attribute selection was set to 0.3 to eliminate redundant features with low variance. Based on observations from the figure, there are ten attributes with variance values exceeding 0.3, which indicates that its behavioural patterns are high. The features that have high variance are the place of birth, income groups, secondary school, university, registration age, entry qualification, the field of study, sponsorship, CGPA, number of activities, and employment status. On the other hand, there are four attributes with a variance value of less than 0.3, namely gender, marital status, residence, and industrial training. The attributes of the state of birth, field of study, and the number of activities show the highest variance value and can be used to show student performance patterns. Additionally, gender, marital status, residence, and industrial training attributes are low-variance features. As all three attributes' marital status has the lowest variance, the other two are close to the threshold, as seen in Figure 2. Furthermore, only 12 attributes with high variance (i.e., place of birth, income groups, secondary school, university, registration age, entry qualification, field of study, sponsorship, CGPA, number of activities, and employment status) are chosen at the end of the unsupervised feature selection, including the drop-out status attribute, and will be used in the next phase.