The Ames Housing dataset, which describes the sale of individual residential property in Ames, Iowa from 2006 to 2010, was compiled by Dean De Cock for use in data science education. It was designed based after the Boston Housing dataset and is now considered a more modernized and expanded version of it. More details of this dataset are described in Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project.
The objective of this data set is to predict the final sale price of each home based on 79 other explanatory variables.
The original data set can be found here.
This dataset can be solved by applying regression techniques, like linear regression, random forest, or gradient boosting.
A good, naive benchmark for regression models is to use the mean.
Performance is evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sale prices.