The week's pick
Reseach Article
Analysis of Wheat Production using Naïve Bayes Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 14 |
| Year of Publication: 2019 |
| Authors: Simrat Kaur Bains, Shaveta Kalsi |
10.5120/ijca2019918908
|
Simrat Kaur Bains, Shaveta Kalsi . Analysis of Wheat Production using Naïve Bayes Classifier. International Journal of Computer Applications. 178, 14 ( May 2019), 38-41. DOI=10.5120/ijca2019918908
Abstract
Data mining is defined as the process in which useful information is extracted from the raw data. In order to acquire essential knowledge it is essential to extract large amount of data. This process of extraction is also known as misnomer. Currently in every field, large amount of data is present and analyzing whole data is very difficult as well as it consumes a lot of time. The prediction analysis is most useful type of data which is performed today. To perform the prediction analysis the patterns needs to generate from the dataset with the machine learning. The prediction analysis can be done by gathering historical information to generate future trends. So, the knowledge of what has happened previously is used to provide the best valuation of what will happen in future with predictive analysis. Crop production analysis is one of the applications of prediction analysis. In this research work, the Naïve Bayes classifier is applied for the wheat production prediction. The Naïve Bayes classifier is compared with SVM and KNN. The Naïve Bayes performs well for the wheat production analysis.
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