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<speak> Let's proceed further.<break strength="x-strong"/> Now let's check whether <break strength="strong"/>we have any null values or not. <break strength="x-strong"/> We do not see null values <break strength="strong"/>or missing values. <break strength="x-strong"/> False indicates no missing values.<break strength="x-strong"/> True indicates no missing values.<break strength="x-strong"/> We are not able to see any null value.<break strength="x-strong"/> Let's check the sum of null values. <break strength="x-strong"/> Here, we don't have any null values. <break strength="x-strong"/> So, all the columns show zero values. <break strength="x-strong"/>Here we can see that <break strength="strong"/>we don't have any null values. <break strength="x-strong"/> Now let's check <break strength="strong"/>whether we have any NAN values or not.<break strength="x-strong"/> If zeros are present in the data, we do not see those values as missing values.<break strength="x-strong"/> In this case, we replace zero values with NAN values.<break strength="x-strong"/> Using this, we will check <break strength="strong"/>whether we have any NAN values. <break strength="x-strong"/> Let's run the cell. <break strength="x-strong"/> Here, we can see that<break strength="strong"/> there are 374 NAN values in insulin.<break strength="x-strong"/> And 227 NAN Values in Skin Thickness<break strength="x-strong"/> We have 35 NAN values in blood pressure.<break strength="x-strong"/> And we have 11 values in Body mass index BMI.<break strength="x-strong"/> </speak>