Data
houses

houses

active ARFF Publicly available Visibility: public Uploaded 16-06-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description: Author: Source: Unknown - Date unknown Please cite: S&P Letters Data We collected information on the variables using all the block groups in California from the 1990 Census. In this sample a block group on average includes 1425.5 individuals living in a geographically compact area. Naturally, the geographical area included varies inversely with the population density. We computed distances among the centroids of each block group as measured in latitude and longitude. We excluded all the block groups reporting zero entries for the independent and dependent variables. The final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value). Bols tols INTERCEPT 11.4939 275.7518 MEDIAN INCOME 0.4790 45.7768 MEDIAN INCOME2 -0.0166 -9.4841 MEDIAN INCOME3 -0.0002 -1.9157 ln(MEDIAN AGE) 0.1570 33.6123 ln(TOTAL ROOMS/ POPULATION) -0.8582 -56.1280 ln(BEDROOMS/ POPULATION) 0.8043 38.0685 ln(POPULATION/ HOUSEHOLDS) -0.4077 -20.8762 ln(HOUSEHOLDS) 0.0477 13.0792 The file cadata.txt contains all th

9 features

medianhousevalue (target)numeric3842 unique values
0 missing
median_incomenumeric12928 unique values
0 missing
housing_median_agenumeric52 unique values
0 missing
total_roomsnumeric5926 unique values
0 missing
total_bedroomsnumeric1928 unique values
0 missing
populationnumeric3888 unique values
0 missing
householdsnumeric1815 unique values
0 missing
latitudenumeric862 unique values
0 missing
longitudenumeric844 unique values
0 missing

19 properties

20640
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
9
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.8
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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