Novel refinement of text captions by using k-nearest neighbors compared with linear regression

Using KNN Classifier against Linear Regression, we want to identify the flower and then publish its name beside it on the same picture. The 17flowers dataset at VGP (Visual Geometry Group) at the University of Oxford consists of a total of 1 360 pictures. Group 1 in Visual Geometry is classified using KNN, while Group 2 is classified using Linear Regression, both with a sample size of (N=5). This paper may be put into practise with the help of jupyter notebook. The effectiveness of the KNN Classifier is measured in terms of the accuracy values computed in comparison to the G power value, which is set at 80%. The KNN Classifier has a 72% success rate on average, whereas the Linear Regression model only has a 67% success rate. This research shows that when it comes to flower categorization, KNN Classifier (mean=72) is a clear winner over Linear regression (mean=67).