## 3 Methodology
### Modelling
#### 3.4.1 J48 Classifier  
C4.5, known as J48, is a classifier first developed by [PERSON] and an extension of the ID3 algorithm. Most of the machine learning classifiers adopt greedy and top-down approaches for making a decision tree. In J48, classification is based on existing observations and training datasets; new data is labelled. While formulating a decision tree, the training dataset is partitioned into smaller partitions by dividing and conquering at each node. The dataset consists of collections of objects and objects that can be either an activity or an event. Each tuple of the dataset contains a class label that defines which object belongs to which class. If the tuples belong to different classes, then further splitting can be performed. While partitioning a dataset, a heuristic approach is followed, which chooses an attribute for the best partition known as the selection measure. The type of branching formation at each node is the responsibility of this selection measure. Like information gain, the Gini index is an example of partitioning the node to multi-label and binary, respectively [32]. For a better working understanding, let us have dataset \(S=X_{1},\ldots,n,C_{i}\), where \(C_{i}\) denotes the dependent variable n representing the number of independent variables, the value of i can be from 1, 2, ::, K. K represents the classes of the dependent variable. At every partition, a new node is added to the decision tree. In S partition, X is chosen for further partitioning into different sets like \(S_{1},S_{2},\ldots,S_{l}\). These new child nodes are then added into the main node S of the decision tree. The primary node S is labelled with text X and newly created partitions \(S_{1},S_{2},\ldots,S_{l}\) are partitioned again recursively. The partition will not be further split into sub-partitions if all records in a partition have identical class labels. Its corresponding leaf will be labelled as a dependent variable. The following steps are followed to construct a decision tree using J48. In step 1, we calculate the Entropy of training set S through Equation (12). \[Entropy(S)=-\sum_{i=1}^{K}\biggl{\{}\biggl{[}\frac{freq(C_{i},S)}{|S|} \biggr{]}log_{2}\biggl{[}\frac{freq(C_{i},S)}{|S|}\biggr{]}\biggr{\}} \tag{12}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Information Gain** & **Wrapper Method** & **Relief-F** & **LASSO** \\ \hline School Gender & School Area & Total Schools & Total Computer Labs \\ Total Schools & Total Playgrounds & Total Teachers & Students without Furniture \\ Total Urban Schools & School Medium & Total Students & Total Rural Schools \\ Building Condition & Total Schools & Total Rural Schools & Total Urban Schools \\ Total Computer Labs & Total Urban Schools & Total Urban Schools & Open Air Class Sections \\ Total Available Classrooms & Classes & Open Air Class Sections & School Gender \\ Students without Furniture & Total Teachers & Total Computer Labs & Building Condition \\ Library Presence & Students with Furniture & Total Science Labs & Total Playgrounds \\ Open Air Class Sections & Total Students & Students with Furniture & Total Students \\ Students with Furniture & Students without Furniture & Deficiency of classrooms \\ Total Rural Schools & Open Air Class Sections & Total Playgrounds & Total Science Labs \\ Total Students & Total Available Classrooms & Total Available Classrooms & Total Teachers \\ Total Playgrounds & Building Condition & Deficiency of Classrooms & School Shift \\ School Shift & Total Science Labs & School Having Electricity & Library Presence \\ Static Result & Matric Result & Matric Result & Matric Result \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Significant feature selection through feature selection methods. where samples in the training set are represented with \(|S|\). \(C_{i}\) is identified as dependent variable, \(i=1,2,:::,K\) represents classes belong to the dependent variable, and freq(\(C_{i}\), \(S\)) has total samples that class \(C_{i}\) contains.