Data

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Accuracy results from testing KNN, Random Forest, and SVC models on each wine type using all sampling methods.

Index	Models	Accuracies	Type of Wine	Sampling Type
0	KNN	0.797959	White	Three Categories
1	KNN	0.859375	Red	Three Categories
2	KNN	0.813077	Red + White	Three Categories
3	Random Forest	0.858163	White	Three Categories
4	Random Forest	0.921875	Red	Three Categories
5	Random Forest	0.848462	Red + White	Three Categories
6	SVC	0.804082	White	Three Categories
7	SVC	0.884375	Red	Three Categories
8	SVC	0.793077	Red + White	Three Categories
9	KNN	0.526316	White	Random Sample Three Categories
10	KNN	0.657895	Red	Random Sample Three Categories
11	KNN	0.394737	Red + White	Random Sample Three Categories
12	Random Forest	0.552632	White	Random Sample Three Categories
13	Random Forest	0.657895	Red	Random Sample Three Categories
14	Random Forest	0.552632	Red + White	Random Sample Three Categories
15	SVC	0.631579	White	Random Sample Three Categories
16	SVC	0.657895	Red	Random Sample Three Categories
17	SVC	0.605263	Red + White	Random Sample Three Categories
18	KNN	0.767347	White	Two Categories
19	KNN	0.690625	Red	Two Categories
20	KNN	0.775385	Red + White	Two Categories
21	Random Forest	0.839796	White	Two Categories
22	Random Forest	0.800000	Red	Two Categories
23	Random Forest	0.845385	Red + White	Two Categories
24	SVC	0.792857	White	Two Categories
25	SVC	0.725000	Red	Two Categories
26	SVC	0.778462	Red + White	Two Categories