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<speak> The maximum value is 1.<break strength="x-strong"/> That is 100 percent accuracy.<break strength="x-strong"/> Let's check the testing scores list.<break strength="x-strong"/> The maximum value is 0.76562. <break strength="x-strong"/>That is 76.562 percent accuracy.<break strength="x-strong"/> Now, we will find the k value corresponding to these maximum values.<break strength="x-strong"/> So now we will see the maximum of this train score.<break strength="x-strong"/> And we will check the number of neighbors it has.<break strength="x-strong"/> Let's run the cell. <break strength="x-strong"/> So the maximum train score is a hundred percent. <break strength="x-strong"/>Here, we got k value 1.<break strength="x-strong"/> And let's check the k value corresponding to the maximum test score. <break strength="x-strong"/> So let's run the cell.<break strength="x-strong"/> We got the maximum test score of 76.56 when k is equal to 11. <break strength="x-strong"/> So, we cannot go with the training score. <break strength="x-strong"/>Let's go with the testing score. <break strength="x-strong"/>So here we got 76.56% accuracy.<break strength="x-strong"/> So here we got the number of neighbors is 11. <break strength="x-strong"/> Let's create a plot to check the best training score and testing score.<break strength="x-strong"/> Now, we will draw sea born line plot.<break strength="x-strong"/> Sea-born line plots depict<break strength="weak"/> the relationship between continuous and categorical values also in a continual data point format.<break strength="x-strong"/> We are creating a figure using the plt dot figure method.<break strength="x-strong"/> We use the s n s dot line plot to draw the graph.<break strength="x-strong"/> <break strength="x-strong"/> </speak>