# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 3 Research Methodology  
This section describes the research methodology in detail. The implementation of the data mining approach is completely described in this section. The Waikato Environment for Knowledge Analysis (WEKA) [30] was used to perform data mining tasks. The research methodology consists of different phases and experiments conducted during this research. The pictorial representation of research methodology is given in Figure 1. \begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Student Attributes** & **Possible Values Used in All Research Papers While Implementing the DM Algorithm** \\ \hline
**Academic Attributes** & Internal and external assessment, lab marks, sessional marks, attendance, Cumulative Grade Point Average (CGPA), semester marks, grade, seminar performance, assignment, attendance, schools marks, previous academic marks, etc. \\ \hline
**Personal Attributes** & Age, gender, height, weight, Emotional Intelligence (EI), student interest, level of motivation, communication, sports person, hobbies and ethnicity, etc. \\ \hline
**Family Attributes** & Qualification, occupation, income, status, support, siblings, responsibilities, etc. \\ \hline
**Social Attributes** & Number of friends, social networking, girls/‘boys’ friends, movies, travel outings, friends’ parties, etc. \\ \hline
**School Attributes** & Teaching medium, accommodation, infrastructure, water and toilet facilities, transportation system, class size, school reputation, school status, class size, school type, teaching methodology, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Student attributes used for predicting performances. The data collection was based on attributes suggested by researchers as the most rational attributes to predict academic performance at secondary level of education. The dataset was collected using an online and physical survey from four different schools based on students' academic, demographic, social, and family attributes. A dataset comprising 1227 records was collected that is currently available online at Kaggle. It includes 21 attributes in total of four different types including demographics, family, social, and academic. A total of 16 attributes remained after a feature selection process. The outcome is categorized into seven classes of grades including A+, A, B+, B, C, D and F. This section demonstrates the detailed description of selected attributes. Table 2 provides a brief description of every attribute that was used in this study. The first step in data pre-processing is data cleansing, which is used to remove all the irrelevant attributes. The dataset consists of 1227 records in total. After detecting the missing values from different features, some records were removed from the dataset. The removal of records does not lead a model towards biasness, if dataset is used for training as a whole for each algorithm instead of subset of the dataset [31; 32]. To reduce the computational complexity while implementing the mining techniques, missing values were also removed. The second step in data preprocessing is feature selection which is used to reduce dimensionality in feature space and obtain better classification results [26] because training on high-dimensional data leads to overfitting of the model. The subset of original features have been picked up through feature selection method which leads to the removal of redundant and obsolete characteristics without losing any important information [26]. This study applied filter-based methods using information gain-based selection to evaluate crucial features which may help in developing good performance models. Filter-based methods are also used to evaluate the classification of the features. Figure 1: Proposed methodology. feature selection is a ranking method, used to rank the attributes according to their rank values by overlooking the remaining ones and then through application to the learning ones. In a rank-based method, values were given to each attribute according to their ranks in building a good model.