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<speak> So I am using terms differently,<break strength="weak"/> people or sometimes patients. <break strength="x-strong"/>So remember <break strength="weak"/>we are talking about these data points. <break strength="x-strong"/> Here we can say that the number of people who don't have diabetes <break strength="weak"/>is double that of people who have diabetes.<break strength="x-strong"/> No diabetes 500<break strength="x-strong"/> diabetes 268<break strength="x-strong"/> So the data set is biased towards the people who don't have diabetes. <break strength="x-strong"/>Let's create a scatter plot <break strength="weak"/>to check the relation between all these features. <break strength="x-strong"/> Here we are creating a scatter plot for the entire data frame.<break strength="x-strong"/> Let's check the plot. <break strength="x-strong"/> So here, we are creating a plot for the old data frame. <break strength="x-strong"/> So here we have diabetes_ df. <break strength="x-strong"/>So, this our old data frame.<break strength="x-strong"/>  Here <break strength="weak"/>we can see the relationship between all columns.<break strength="x-strong"/>  So, let's check the relation between features.<break strength="x-strong"/>  Let's see <break strength="weak"/>which column has a good correlation.<break strength="x-strong"/>  Let's see this plot.<break strength="x-strong"/> One feature is skin thickness, and this feature is BMI. <break strength="x-strong"/>Here also we have skin thickness and BMI. <break strength="x-strong"/> So, these two are very well correlated. <break strength="x-strong"/>With the increase in thickness, the BMI is also increasing. <break strength="x-strong"/> </speak>