From the course: Data Science Foundations: Data Assessment for Predictive Modeling

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Solution: Practice describe data with the UCI heart dataset

Solution: Practice describe data with the UCI heart dataset

From the course: Data Science Foundations: Data Assessment for Predictive Modeling

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Solution: Practice describe data with the UCI heart dataset

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(upbeat music) - [Instructor] Okay, let's review what I've done. So I started by going through and looking for IDs that were strange because it seems to me that's a variable that should be present and consistent. Then remember that this column, column B is supposed to always be a zero. It's where their social security numbers were. So I was focused on that as well. Notice that the ages now are all within a reasonable range. There's no negative nine for age. Very few rows were taken out by the way, sex variables are now consistent, no negative nines. Again, that would normally be something basic information that you would have for everybody. Then we get into a whole bunch of columns that we expect there to be some missing data, and we don't attribute to a data loading problem. If you go to the far right-hand side, the rows that I've taken out, junk still looks a bit strange. But after all the column is called junk, and…

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