Data
house_sales

house_sales

active ARFF CC0 Public Domain Visibility: public Uploaded 19-08-2019 by Thomas Schmitt
0 likes downloaded by 4 people , 14 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It contains 19 house features plus the price and the id columns, along with 21613 observations. It's a great dataset for evaluating simple regression models.

20 features

price (target)numeric4028 unique values
0 missing
id (row identifier)numeric21436 unique values
0 missing
datestring372 unique values
0 missing
bedroomsnumeric13 unique values
0 missing
bathroomsnumeric30 unique values
0 missing
sqft_livingnumeric1038 unique values
0 missing
sqft_lotnumeric9782 unique values
0 missing
floorsnumeric6 unique values
0 missing
waterfrontnumeric2 unique values
0 missing
viewnumeric5 unique values
0 missing
conditionnumeric5 unique values
0 missing
gradenumeric12 unique values
0 missing
sqft_abovenumeric946 unique values
0 missing
sqft_basementnumeric306 unique values
0 missing
yr_builtnumeric116 unique values
0 missing
yr_renovatednumeric70 unique values
0 missing
zipcodenominal70 unique values
0 missing
latnumeric5034 unique values
0 missing
longnumeric752 unique values
0 missing
sqft_living15numeric777 unique values
0 missing
sqft_lot15numeric8689 unique values
0 missing

62 properties

21613
Number of instances (rows) of the dataset.
20
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.
18
Number of numeric attributes.
1
Number of nominal attributes.
Maximum mutual information between the nominal attributes and the target attribute.
70
The minimal number of distinct values among attributes of the nominal type.
90
Percentage of numeric attributes.
2009.89
Third quartile of means among attributes of the numeric type.
70
The maximum number of distinct values among attributes of the nominal type.
-0.49
Minimum skewness among attributes of the numeric type.
5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
13.06
Maximum skewness among attributes of the numeric type.
0.09
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
4.16
Third quartile of skewness among attributes of the numeric type.
367127.2
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.92
First quartile of kurtosis among attributes of the numeric type.
850.68
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
1.96
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
38.44
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
32006.05
Mean of means among attributes of the numeric type.
0.73
First quartile of skewness among attributes of the numeric type.
-324979.42
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.62
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
70
Average number of distinct values among the attributes of the nominal type.
3.06
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.13
Mean skewness among attributes of the numeric type.
65.98
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
24397.92
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.46
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.68
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
15.27
Second quartile (Median) of standard deviation of attributes of the numeric type.
285.08
Maximum kurtosis among attributes of the numeric type.
-122.21
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
540088.14
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
38.21
Third quartile of kurtosis among attributes of the numeric type.

9 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_relative_squared_error - target_feature: price
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task