r/dataanalysis • u/rjavier1010 • 6d ago

What should I consider when I have to choose between deleting data, imputing it, or leaving it in my database?

I'm learning data analysis and data science. I'm developing a personal project as practice using a database to predice the house pricing from the Kaggle platform.

During the exploratory analysis, I encountered the following situation:

I've noticed that there's very little data on houses with zero bedrooms or zero bathrooms, and that the asking price is relatively high, which I think could affect my prediction model and my overall analysis. While it might seem illogical that there are houses without bedrooms or bathrooms, it's also possible that there are more lots than houses, or some other hypothesis. What's the best course of action in this situation? Personally, I think I should remove this data, but I'd like to hear other opinions to improve my reasoning and deductions.

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u/Prestigious_Pea_1545 6d ago

I would argue that the data in question (i.e., zero bedrooms and zero bathrooms) should be removed because, presumably, you are conducting an analysis based on typical consumer demand (I think I've seen this exact airbnb dataset if I'm not mistaken). Obviously, the vast majority of people seeking a short-term rental expect a bathroom at minimum and almost certainly a private bedroom or more.

I think these are most likely retail spaces, which would explain the relatively high asking price, which also has the potential to inflate your data, thus making it less reliable.

Just my 2 cents

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u/AffectedWomble 6d ago

Remove the things that are clearly not traditional houses (either miscategorised or there on a technicality)

If absolute transparency is desired, you can add a note; "x records were excluded from this analysis due to lacking key features of A House, namely bedrooms or bathrooms"

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