Sometimes we have to deal with manually entered data, which means there is a good chance that the data needs to be cleaned for consistency due to the inevitable errors that creep in when typing in data, not to speak of any inconsistencies between individuals entering data. ...
A popular beginners machine learning problem is the prediction of housing prices. A frequently used data set for this purpose uses housing prices in California along some additional gathered through the 1990 Census. One such data set is available here at Kaggle. Unfortunately, that data set is rather old. And I live in North Carolina, not California! So I figured I might as well create a new housing data set, but this time with more up-to-date information and using North Carolina as the state to be analyzed. One thing that may be interesting about North Carlina as compared to California is the position of major populations centers. In California, major population centers are near the beach, while major population centers in North Carolina are in the interior of the state. Both large citites and proximity to the beach tend to correlate with higher housing prices. In California, unlike in North Carolina, both of these go together. ...