# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 3 Methodology
### Data Preparation
This restriction must be addressed for implementing and developing machine learning models. This implies that all characteristics, including categories or nominal variables, must be transformed into numeric variables before being fed into the clustering and classification model. All these have been dealt with in the data transformation process. Machine learning models learn how to map input variables to output variables. As a result, the scale and distribution of the domain data may differ for each variable. Input variables may have distinct units, which means they may have different scales. Differences in scaling among input variables may increase the difficulty of the modelled problem. Large input values (for example, a spread of hundreds or thousands of units) can result in a model that learns large weight values. A model with large weight values is frequently unstable, so it may perform poorly during learning and be sensitive to input values, resulting in a larger generalisation error. On top of that, normalization was performed using the StandardScaler or z-transformation and MinMaxScaler methods to form datasets for three different models. The first method, StandardScaler, allows each attribute's values to be in the same range so that a comparison can be made. Z-transformation normalisation refers to normalising every value in a dataset such that the mean of all values is 0 and the standard deviation is 1 [38]. The equation to perform the z-transformation normalisation on every value in a dataset is as Equation (1), where \(x_{j}\) is the input value of the sample \(j\), \(\mu\) is the sample mean, and \(\sigma\) is the standard deviation of the sample data. \[z_{j}=\frac{x_{j}-\mu}{\sigma} \tag{1}\]  
In the second method, MinMaxScaler converts all attributes into a range [0,1], which means the minimum value of the attribute is zero, and the maximum value of the attribute is one [38]. The mathematical formula for MinMaxScaler is defined as Equation (2), where the minimum \(x_{min}\), and maximum \(x_{max}\) values correspond to the normalised value x.  
\[x_{j}=\frac{x_{j}-x_{min}}{x_{max}-x_{min}} \tag{2}\]  
**Descriptive Analysis. ** Table 3 shows the descriptive analysis of the student's dataset after it has been transformed into numerical data. A variable containing categories that lack a natural order or ranking is referred to as a nominal scale. Calculations like a mean, median, or standard deviation would be pointless for nominal variables because they are arbitrary. Hence, Table 3 does not generate the mean, median, or standard deviation for the nominal variables. Descriptive analysis of student's dataset. Attributes with the highest standard deviations are CGPA and number of activities (0.922 and 1.684, respectively). Besides these two, a huge gap between the other attributes can be observed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Count** & **Mean** & **Std** & **Min** & **0.25** & **0.5** & **0.75** & **Max** \\ \hline University & 117069 & - & - & 1 & 1 & 2 & 3 & - \\ Registration Age & 117069 & - & - & 1 & 1 & 2 & 2 & - \\ Qualification & 117069 & 1.823 & 0.739 & 1 & 1 & 2 & 2 & 3 \\ Field of Study & 117069 & - & - & 1 & 3 & 4 & 5 & - \\ Sponsorship & 117069 & - & - & 1 & 2 & 2 & 3 & - \\ CGPA & 117069 & 2.764 & 0.922 & 1 & 2 & 3 & 3 & 4 \\ Industrial Training & 117069 & - & - & 0 & 0 & 1 & 1 & - \\ Number of Activities & 117069 & 2.486 & 1.684 & 0 & 1 & 2 & 4 & 5 \\ Employment Status & 117069 & - & - & 1 & 1 & 1 & 2 & - \\ Drop-out Status & 117069 & 0.825 & 0.38 & 0 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive analysis of student’s dataset. **Feature Selection. ** Too many attributes in educational datasets can cause a curse of dimensionality and difficulties when processing and analysing the data. Moreover, calculation of distance by clustering algorithms may not be effective for high-dimensional data. To solve this problem, feature selection methods are applied to find the best attributes for this study.