Crop production analysis is one of the applications of prediction analysis. This study is related to paddy production. In the previous research work, the SVM and KNN algorithm is implemented to analyze prediction. To improve the accuracy of the paddy production, the hybrid classifier will be designed based on K-mean clustering and Naive Bayes classifier. The presented and earlier algorithms will be applied in python and it is expected that accuracy will be improved with a reduction in execution time. The performance of SVM, KNN, and Naive Bayes is compared for the wheat production prediction. Naive Bayes is the best classifier for the wheat production prediction as per the obtained analytic results.
| Autorius: | Pankaj Bhambri |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 76 |
| ISBN-10: | 6202683309 |
| ISBN-13: | 9786202683302 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Design of Paddy Crop Production Technique: Using K-Mean, Naive Bayes, KNN and SVM Classifiers“
