This section describes main characteristics of the house16H data set and its attributes:
General information
House-16H data set |
Type | Unsupervised | Origin | Real world |
Features | 17 | (Real / Integer / Nominal) | (10 / 7 / 0) |
Instances | 22784 | Missing values? | No |
Attribute description
Attribute | Domain | Attribute | Domain |
P1 | [2,7322564] | P27p4 | [0,0.7057357] |
P5p1 | [0.125,0.9230769] | H2p2 | [0,0.9751773] |
P6p2 | [0,1] | H8p2 | [0,1] |
P11p4 | [0,0.9172546] | H10p1 | [0.0032573,1] |
P14p9 | [0,0.5118719] | H13p1 | [0,1] |
P15p1 | [0.0541562,1] | H18pA | [0,1] |
P15p3 | [0,0.9433249] | H40p4 | [0,1] |
P16p2 | [0.2337023,1] | Price | [0,500001] |
P18p2 | [0,0.125] |
Additional information
This database was designed on the basis of data provided by US Census Bureau [http://www.census.gov]. The data were collected as part of the 1990 US census. These are mostly counts cumulated at different survey levels. For the purpose of this data set a level State-Place was used. Data from all states was obtained. Most of the counts were changed into appropriate proportions.
These are all concerned with predicting the median price of the house in the region based on demographic composition and a state of housing market in the region. A number in the name signifies the number of attributes of the data set. A following letter denotes a very rough approximation to the difficulty of the task. For Low task difficulty, more correlated attributes were chosen as signified by univariate smooth fit of that input on the target. Tasks with High difficulty have had their attributes chosen to make the modelling more difficult due to higher variance or lower correlation of the inputs to the target.
In this section you can download some files related to the house16H data set:
- The complete data set already formatted in KEEL format can be downloaded from
here
.
- The header file associated to this data set can be downloaded from here
.
- This is not a native data set from the KEEL project. It has been obtained from the Bilkent University Function Approximation Repository. The original page where the data set can be found is: http://funapp.cs.bilkent.edu.tr/DataSets/.
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