## 3 Data Set and Preprocessing Steps  
In this study, we considered the \"Student_performance_data_csv\" data set from the Kaggle platform, representing the academic achievement of students and created to examine the relationship between students' academic achievement and different variables [25]. This data set is used to analyze the factors affecting students' academic performance, and it consists of a total of 2392 points of data, which include demographic information, such as the age and gender of the students; academic information, including the frequency of attendance and course study time; and activity participation information, including participation in sports, music, or volunteer activities. In this study, the overall Grade Point Average (GPA) of the student was determined to be the dependent variable, and was the target to be estimated. Grade class was not included in the analysis since it expresses the same concept as GPA. As it is a variable that repeats the same information, it was thought that it might consume unnecessary space in the model and cause overfitting problems. Student ID was not included in the analysis, because it is a unique identifier that identifies the student and has no explanatory function relative to academic performance. Correlation analysis was performed to evaluate the relationships between the variables in the data set. As a result of the correlation analysis, the heat map of which is given in Figure 1, high linear correlation was found between some variables. Since this may lead to overfitting of the model, some of the variables with high correlation were removed from the data set. Figure 1: Correlation matrix heat map. Figure 1 shows that the academic achievement of the students decreases significantly as the number of absences increases. The data also show that students' grade levels and academic achievement largely overlap. In addition, weak but positive relationships were found between some variables, such as parental support and tutoring and GPA. On the other hand, variables such as age, gender, ethnicity, and participation in music or sports activities have very low correlations with GPA. This suggests that these variables do not have a direct determining effect on academic achievement. In sum, this correlation analysis reveals that absences and grade level are most strongly associated with students' academic achievement. The data set comprises various academic and social variables associated with 2392 students. As shown in Table 1, the average number of absences is 14.54, with total absence duration ranging from 0 to 29 days, and a high standard deviation of 8.47. This situation reveals significant differences among students, in terms of absences. The parental support variable was coded between 1 and 4 and resulted in a mean of 2.12; most students received moderate support. The average weekly study time was 9.77 h, but this time shows a wide distribution, ranging between 0.001 and 19.97 h. Only 30 percent of the students took private lessons, and 38 percent participated in social activities. GPA, which represents academic performance, has a mean of 1.91 out of 4.00, with 50% of students scoring between 1.17 and 2.62. These results indicate significant differences among students in factors such as academic achievement, study time, parental support, and social activity participation, and that the effects of these variables on achievement are worth analyzing. Figure 2 illustrates the relationships between GPA and the four variables with the highest correlation coefficients: absences, grade class, parental support, and weekly study time. A strong negative correlation is observed between GPA and absences, suggesting that increased absenteeism reduces academic performance. Conversely, both weekly study time and parental support exhibit positive associations with GPA. These visualizations align with numerical correlation analysis and provide valuable insights into the impacts of these variables on students' academic success.