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house_sales_reduced

house_sales_reduced

active ARFF Public Domain (CC0) Visibility: public Uploaded 31-08-2020 by Alexane Jouniaux
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Author: https://www.kaggle.com/harlfoxem/ https://www.kaggle.com/harlfoxem/ Source: [original](https://www.kaggle.com/harlfoxem/housesalesprediction) - 2016-08-25 Please cite: 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.

21 features

price (target)numeric4028 unique values
0 missing
attribute_0numeric21613 unique values
0 missing
idnumeric21436 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
zipcodenumeric70 unique values
0 missing
latnumeric5034 unique values
0 missing
longnumeric752 unique values
0 missing
sqft_living15numeric777 unique values
0 missing
sqft_lot15nominal8689 unique values
0 missing

19 properties

21613
Number of instances (rows) of the dataset.
21
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.
20
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of instances having missing values.
-324979.42
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
95.24
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
4.76
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.
0
Percentage of binary attributes.

7 tasks

0 runs - estimation_procedure: 33% Holdout set - 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
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