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<speak> Remember, this plot is for an old data frame with missing values. <break strength="x-strong"/> So this data is not well distributed.<break strength="x-strong"/>  I mean, we don't have proper correlation also. <break strength="x-strong"/> Let's plot a scatter plot for the modified data frame.<break strength="x-strong"/> So here we have diabetes _df_copy, <break strength="weak"/>so which is our modified data frame.<break strength="x-strong"/> We have removed null values <break strength="weak"/>and replaced them with mean and medians.<break strength="x-strong"/> So, let's check that. <break strength="x-strong"/> Here <break strength="weak"/>we can see that we have taken hue as the outcome.<break strength="x-strong"/> What is the hue parameter?<break strength="x-strong"/> The hue parameter is useful<break strength="weak"/> for the grouping variable that will produce points with different colors.<break strength="x-strong"/> So let's check the visualization for various plots.<break strength="x-strong"/> Here outcome 0 is blue.<break strength="x-strong"/> Outcome 1 is orange in color. <break strength="x-strong"/> The orange color means they have diabetes.<break strength="x-strong"/> Blue color means they don't have diabetes. <break strength="x-strong"/> Let's take the skin thickness and BMI column again.<break strength="x-strong"/> Here is the skin thickness, and here is the BMI.<break strength="x-strong"/> Here we can see that with an increase in skin thickness, many people got diabetes.  <break strength="x-strong"/> Let's check the column where the uncertainties are high.<break strength="x-strong"/> I mean, this is mixed data. <break strength="x-strong"/>So all these data points are mixed.<break strength="x-strong"/>  It is not easy to separate them.<break strength="x-strong"/> And here, we can see that the age column and glucose column.<break strength="x-strong"/> </speak>