Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

Table 1

Description of student performance dataset (n = 1000).

Factors	Categories	Frequency	Percentage (%)
Gender	Female	518	51.8
	Male	482	48.2
Race/ethnicity	Group A	89	8.9
	Group B	190	19.2
	Group C	319	31.9
	Group D	262	26.2
	Group E	140	14.0
Parental level of education	Some college	226	22.6
	Some high school	179	17.9
	High school	196	19.6
	Bachelor’s degree	118	11.8
	Master’s degree	59	5.9
	Associate’s degree	222	22.2
Lunch	Standard	645	64.5
	Free/reduced	355	35.5
Test preparation	None	642	64.2
	Completed	358	35.8
Math score	0–10	2	0.2
	11–20	2	0.2
	21–30	12	1.2
	31–40	34	3.5
	41–50	100	10.0
	51–60	189	18.9
	61–70	270	27.0
	71–80	215	21.5
	81–90	126	12.6
	91–100	50	5.0
Reading score	0–10	0	0
	11–20	1	0.1
	21–30	7	0.7
	31–40	19	1.9
	41–50	70	7.0
	51–60	178	17.8
	61–70	238	23.8
	71–80	252	25.2
	81–90	173	17.3
	91–100	62	6.2
Writing score	0–10	1	0.1
	11–20	2	0.2
	21–30	7	0.7
	31–40	25	2.5
	41–50	89	8.9
	51–60	177	17.7
	61–70	243	24.3
	71–80	248	24.8
	81–90	140	14.0
	91–100	68	6.8