# A Study on Dropout Prediction for University Students Using Machine Learning
## 3 Proposed Method
### Feature Selection
From the Scholarship table, scholarship received in the last semester is obtained for each student, which attribute is denoted as Scholar in Table 5. Also, from the Counsel, ExtraCourse, and BookLoan tables, the number of counseling sessions, extracurricular courses attended, and book rentals performed in the last semester can be obtained for each student. Note that we only consider information from the last semester when extracting the attribute values from the source tables. This is because it was determined that the attribute \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attribute** & **Type** & **Description** & **Source Table** \\ \hline Grade & Number & Average grade & Grade \\ NumF & Number & Number of F grades in the last semester & Grade \\ NumSem & Number & Number of semesters enrolled & AcademicS\(\ldots\) \\ NumAbs & Number & Number of consecutive semesters of leave-of-absence & AcademicS\(\ldots\) \\  & & right before graduation or dropout & \\ Dropout & Boolean & Final status: dropout(1) or non-dropout(0) & AcademicS\(\ldots\) \\ Scholar & Number & Scholarship received in the last semester & Scholarship \\ NumCouns & Number & Number of counseling in the last semester & Counsel \\ NumExtra & Number & Number of participation in extra curriculum subjects in & ExtraCourse \\  & & the last semester enrolled & \\ NumBook & Number & Number of book loans in the last semester & BookLoan \\ Dept & Number & Department number & StudentInfo \\ AdmType & Category & Admission type: Freshman(0), Transfer(1) & StudentInfo \\ Region & Category & Region of a high school graduated & StudentInfo \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Candidate attributes of the summary table whose values can be extracted from the source tables in Table 2.  
values for each semester were independent of each other. From the StudentInfo table, the department, admission type, and region information can be obtained, which attributes are denoted as Dept, AdmType, and Region, respectively. Regarding 2, the summary table can arithmetically contain up to 150 attributes. In order to achieve high classification accuracy, only attributes that have a high correlation with the Dropout attribute need to be added to the table; the more attributes that are highly correlated with student dropout are included and learned, the higher prediction accuracy can be obtained. As a criterion for the selection, the correlation coefficient with the Dropout attribute was examined for each candidate. In the proposed method, attributes whose coefficient value was greater than or equal to 0.01 were chosen. Figure 1 shows the attributes chosen for the summary table. The attribute with the highest correlation with Dropout was Grade, followed by NumSem, NumF, Scholar, and NumAbs. NumSem was highly correlated with Grade and Scholar, while Grade was highly correlated with NumF. To obtain the correlation coefficients between the attributes, the corr() function of the data frame in Python was used. For visualization, the heatmap() function in the Seaborn package [35] was used. Table 6 shows an example of the summary table constructed from the records shown in Tables 3 and 4. The summary table consists of 11 attributes shown in Figure 1 in addition to SID as a primary key, and it has 20,050 records, which is equal to the number of students registered in the academic information system of Sahmyook University.