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

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Solution: Quantifying missing data with the UCI heart dataset

Solution: Quantifying missing data with the UCI heart dataset

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

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Solution: Quantifying missing data with the UCI heart dataset

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(upbeat music) - [Instructor] Okay, I hope you enjoyed giving that a try, let me walk you through how I chose to solve the problem. So I'm in a spreadsheet called UCI_ Heart_chapter 11_ solution, which I provided for you in the solutions folder. And if you go to the far right hand side, what I've done is use the COUNTIF function. Let me go ahead and show you that formula bar. I've used the COUNTIF function to count negative nines. Obviously, I don't attempt to count the negative nines in the name or location columns, but in the columns that are appropriate. And there's no obvious pattern here, so I'll have a lot to ask a subject-matter expert but that's not all. Remember, we had to do columns. So if you scroll all the way down to the bottom I've got COUNTIF going across the bottom as well and because we've got hundreds of rows I've copied and paste the IDs down at the bottom so that I can see them better. Now, please…

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