We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



Missing Housing Data

Drop all listings with missing square footage data from your dataset, as they only comprise 20% of the overall total.

Use a simple imputation method, like replacing the missing values with the mean or median of the distribution.

Ignore the square footage data, as it may not greatly impact the prediction of housing prices.

Use a more advanced imputation method, such as a nearest neighbors method, to approximate the missing square footage.

Build models of varying sizes, of training data with less than 80% of the dataset, to see the learning curve.

Missing Housing Data

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