Effectiveness of a machine learning classifier model
DOI:
https://doi.org/10.29070/4sfq6836Keywords:
Machine Learning, Predictive Analytics, AlgorithmsAbstract
As a statistical tool, predictive modelling may foretell how something will act in the future. In order to foretell how people will act in the future, machine learning has become a popular tool. Determining which of the many accessible algorithms is best suited to the data at hand is an intriguing challenge. The field of study that finds the greatest value in predictive modelling is educational data mining. Accurately predicting undergraduate students' grades has several benefits for both students and teachers. Students might be more motivated to choose their future endeavours with the support of early prediction. Using data gathered from undergraduate studies, this study displays the outcomes of many machine learning methods. It uses data obtained from undergraduate studies to assess the efficacy of several machine learning methods. Choosing the proper characteristics and the right prediction algorithm are two big problems that are addressed.
References
Acharjya, D. P. (2020). A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique. Journal of Medical Systems, 44(1), 27.
Narayan, S., & Sathiyamoorthy, E. (2019). A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases. Neural Computing and Applications, 31(1), 93-102.
Bucholc, M., Ding, X., Wang, H., Glass, D. H., Wang, H., Prasad, G., ... & Finn, D. P. (2019). A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert systems with applications, 130, 157-171.
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.
Zhao, D., Liu, H., Zheng, Y., He, Y., Lu, D., & Lyu, C. (2019). A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Medical & biological engineering & computing, 57(4), 901-912.
Vinodhini Gopalakrishnan et.al.,(2017),”Patient opinion mining to analyze drugs satisfaction using supervised learning”,Journal of Applied Research and Technology, Vol.15, Iss 4,pp.311-319.
Wang, D., et.al.,(2018). A content-based recommender system for computer science publications. Knowledge-Based Systems, Vol.157, pp.1–9.
Zineb Nassr, et.al.,(2019), A comparative study of sentiment analysis approaches. ,SCA '19. Vol.91, pp.1–8.
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