Abstract
Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
