By A Mystery Man Writer
This study uses five regression techniques to analyse students’ first-year cumulative grade point average (CGPA) and predict their final graduation CGPA and linear regression is the model with the mean closest to zero that best fits the data. Data mining and regression techniques are important methods that we can use to predict students’ performance to inform decision making. This study uses five regression techniques to analyse students’ first-year cumulative grade point average (CGPA) and predict their final graduation CGPA. The data set used in this study is that of programming and software development students at Kano Informatics Institute. The results and the grades obtained by 163 students forms the sample data used in the study. The forecast error, mean forecast error and mean absolute forecast error are all calculated. Dickey–Fuller’s stationary t-test is performed for all the regressions analysis values using the Python programming language to determine the mean and if the data is centred on the mean. We use the stationary t-test to test the null and alternative Dickey–Fuller’s hypotheses to compare our P-values and critical values for all regressions analyses done. The results show that the P-values obtained for all the regressions are small and less than the critical value. However, linear regression is the model with the mean closest to zero, and, according to Dickey–Fuller’s statistics, it is the model that best fits our data.
Pictorial Representation of the entire process
Predicting Student's Final Graduation CGPA Using Data Mining and
Student's Performance Prediction Using Data Mining Technique
A survey - data mining frameworks in credit card processing
KR20060001923A - 퍼지 로직을 이용한 규칙베이스와 사례베이스를 통합
PDF) PREDICTING TIMELY GRADUATION OF POSTGRADUATE STUDENTS USING RANDOM FORESTS ENSEMBLE METHOD
Salim DANBATTA, Lecturer, Ph.D.
Salim DANBATTA, Lecturer, Ph.D.
Artificial Neural Network with Learning Analytics for Student
Artificial Neural Network with Learning Analytics for Student
PDF) PREDICTION OF STUDENTS' ACADEMIC PERFORMANCE USING
Educational data mining to predict students' academic performance
PDF) Classifiers ensemble and synthetic minority oversampling
PDF) An Enhanced Multiple Classfier System for Predicting Students' Academic Outcome
Master of Business Administration (General) - SDE Programme