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<speak> <break strength="x-strong"/> And here, we can say that 268 people out of 768 people have diabetes which is 1, and the others are 0. <break strength="x-strong"/> We cannot mis classify the items. <break strength="x-strong"/>Suppose, if the person has diabetes.<break strength="x-strong"/> And you predict that <break strength="strong"/>the person doesn't have diabetes.<break strength="x-strong"/> Then the person will not take medication.<break strength="x-strong"/> So, we cannot take the risk because this comes under the health domain.<break strength="x-strong"/> As it is a health sector project, we require big data set.<break strength="x-strong"/> Here the data set is small.<break strength="x-strong"/> Hence we cannot get an accurate result, but we will try to get a good prediction result in this model. <break strength="x-strong"/><break strength="x-strong"/> The next step is to import all the required libraries.<break strength="x-strong"/> So let's import all the required libraries.<break strength="x-strong"/> We are importing NumPy as n p, pandas as p d.<break strength="x-strong"/> We use NumPy and pandas packages for data analysis and<break strength="strong"/> data cleaning.<break strength="x-strong"/> We are importing matplotlib.pyplot as p l t, seaborn as s n s.<break strength="x-strong"/> We use matplotlib and seaborn for <break strength="strong"/>data visualization.<break strength="x-strong"/> We are importing plot decision regions.<break strength="x-strong"/> Plot decision regions is a function for plotting decision regions of classifiers<break strength="strong"/> in 1 or 2 dimensions.<break strength="x-strong"/> We are importing missing n o as m s n o.<break strength="x-strong"/> We can visualize missing values <break strength="strong"/>N a N values<break strength="strong"/> using Missing n o Library.<break strength="x-strong"/> We are importing various other libraries from s k learn.<break strength="x-strong"/> Sci kit learn is a machine learning package.<break strength="x-strong"/> We are also importing Grid Search C V.<break strength="x-strong"/> Grid search is used <break strength="strong"/>to find the optimal hyper parameters of a model, which results in<break strength="strong"/> the most accurate predictions.<break strength="x-strong"/> We are importing classification report, confusion_matrix, and K Nearest Neighbors.<break strength="x-strong"/> K nearest neighbors is <break strength="strong"/>a simple classification algorithm.<break strength="x-strong"/> We use classification reports<break strength="strong"/> to compare classification models.<break strength="x-strong"/> We use a confusion matrix <break strength="strong"/>to find the performance of a model.<break strength="x-strong"/> Lets we run the cell.<break strength="x-strong"/> So we have imported all the required libraries.<break strength="x-strong"/> <break strength="x-strong"/> </speak>