Data
Brazilian_houses

Brazilian_houses

active ARFF Publicly available Visibility: public Uploaded 18-06-2022 by Leo Grin
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  • Computer Systems Machine Learning
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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 "regression on categorical and numerical features" benchmark. Original description: Author: Kaggle Source: [original](https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent) - 20-03-2020 Please cite: This dataset contains 10962 houses to rent with 13 diferent features. Outliers Some values in the dataset can be considered as outliers for further analyses. Bear in mind that the Web Crawler was used only to get the data, so it's possible that errors in the original data exist. Changes in data between versions of dataset Since the WebCrawler was ran in different days for each version of dataset, there may be differences like added or deleted houses (as well as added cities). Notes: 1) This dataset corresponds to the 2nd version of the original dataset ("houses_to_rent_v2.csv"). 2) The value '-' in the attribute floor was replaced by '0' as the data contributor stated that this refers to houses with just one floor (see https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent/discussion).

12 features

total_(BRL) (target)numeric5751 unique values
0 missing
citynominal5 unique values
0 missing
areanumeric517 unique values
0 missing
roomsnumeric11 unique values
0 missing
bathroomnumeric10 unique values
0 missing
parking_spacesnumeric11 unique values
0 missing
animalnominal2 unique values
0 missing
furniturenominal2 unique values
0 missing
hoa_(BRL)numeric1679 unique values
0 missing
rent_amount_(BRL)numeric1195 unique values
0 missing
property_tax_(BRL)numeric1243 unique values
0 missing
fire_insurance_(BRL)numeric216 unique values
0 missing

19 properties

10692
Number of instances (rows) of the dataset.
12
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.
3
Number of nominal attributes.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.
16.67
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.09
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
75
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
25
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

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