## 4 Grade Prediction Method Based on DNNs
### Pearson Correlation Coefficient, Definitions of Distance and Thought on the KNN Algorithm
#### 5.1.5 KNN Algorithm  
The k-nearest neighbour (KNN) [42] algorithm is one of the popular algorithms in ML to perform supervised learning classification and regression. If K samples closest to the centre of a sample are reported by a certain distance (Euclidean distance) measurement, the K samples belong to this category. Most Similar Sample Recommendation Model Based on the Distance Proximity Algorithm Improved with the Pearson Correlation Coefficient  
As shown in Table 4, a Pearson correlation table is established using the basic dataset and grade data to be predicted. College English 2 demonstrated a strong correlation with College English 4. Although English courses correlate with Physics and Mathematics courses, they are less relevant than similar English courses. Metallurgical Physical Chemistry 1 has a close relationship with Physics and Chemistry and correlates with Mathematics courses whose correlation coefficient is higher than that of English courses. Another example is the Employment Guidance course for college students, which does not show a great correlation with the Physical Chemistry, Mathematics, and English courses. Their scores only show a weak correlation. The correlation coefficient of the course 'Physical Chemistry of Metallurgy' can be taken as an example. Based on the correlation coefficient, when taking the Euclidean distance, Manhattan distance, Chebyshev distance, and their improved algorithms as the distance criteria, and the test sample as the centre for clustering with the KNN algorithm, the top five similar student data can be obtained. Considering the minimum value of each group and comparing the correlation coefficients in Table 5, the specific situation can be observed. Based on multiple common distance standards, the data most similar to the learning situation of the target student is Student No. 31. Based on multiple distance standards of the Pearson correlation coefficient, the data most similar to the learning situation of the target student are Student No. 217. The grade data of Student Nos. 31 and 217 are obtained from the original dataset. As per expert experience, the connection between each course and target course is obtained. The courses that are highly and less correlated with the target course are divided into groups. Then, the grade data between the target students and most similar students reported by the two algorithms are compared; moreover, the comparison line graph is drawn and shown in Figures 5 and 6. As shown in Figure 5, the trend of the blue line is closer to that of the red line and more dispersed than that of the orange line. The difference between Student Nos. 0 and 217 in corresponding courses is smaller than that between Student Nos. 0 and 31. As shown in Figure 6, the shape of the blue and red lines is similar, indicating that the difference between Student Nos. 31 and 0 is small; they are the closest in the case of clustering when the correlation is not considered. The red and blue lines considerably differ in direction, indicating that Student No. 217 in the normal range benchmark clustering has not entered the top five sorted neighbourhood. Nevertheless, Student Nos. 217 and 0 are the closest in consideration of correlation. With the enlargement of the sample data, the student most similar to the target student can be determined using a correlation coefficient from whom the corresponding course learning methods can be obtained. The methods will be