{"title":"Cow Milk Quality Grading using Machine Learning Methods","authors":"Shubhangi Neware","doi":"10.47164/ijngc.v14i1.1005","DOIUrl":null,"url":null,"abstract":"Milk is considered as complete food as it contains rich set of proteins and vitamins. Therefore determining quality of cow milk plays an important role in today’s research. In this paper four methods are implemented to check quality of cow milk using dataset consists of 1059 milk samples taken from various cows. Three grades of milk grade A, B, C are considered based on different features of cow milk. Various machine learning methods K Nearest neighbors, Logistic regression, Support Vector machine and ANN are implemented. Accuracy of these methods is then compared. It has been observed that the results of KNN (n=3) is more accurate amongst all four methods implemented in the proposed research work.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"28 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Next-Generation Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47164/ijngc.v14i1.1005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Milk is considered as complete food as it contains rich set of proteins and vitamins. Therefore determining quality of cow milk plays an important role in today’s research. In this paper four methods are implemented to check quality of cow milk using dataset consists of 1059 milk samples taken from various cows. Three grades of milk grade A, B, C are considered based on different features of cow milk. Various machine learning methods K Nearest neighbors, Logistic regression, Support Vector machine and ANN are implemented. Accuracy of these methods is then compared. It has been observed that the results of KNN (n=3) is more accurate amongst all four methods implemented in the proposed research work.