Data
HousingPrices

HousingPrices

active ARFF Public Domain (CC0) Visibility: public Uploaded 22-03-2024 by Iwo Godzwon
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Description: This dataset contains information about real estate properties in Amsterdam. It includes details such as address, zip code, price, area size, number of rooms, longitude, and latitude coordinates. Columns description: - Unnamed: 0: An identifier for each property listing. - Address: The street address of the property in Amsterdam. - Zip: The postal code of the property location. - Price: The price of the property in Euros. - Area: The size of the property in square meters. - Room: The number of rooms in the property. - Lon: The longitude coordinate of the property location. - Lat: The latitude coordinate of the property location. Use case: This dataset is valuable for real estate agencies, property investors, and researchers interested in the Amsterdam housing market. It can be used to analyze housing trends, pricing variations, and property characteristics across different neighborhoods in Amsterdam. The geographical coordinates also allow for mapping and spatial analysis of the properties. Additionally, this dataset can be used for predictive modeling to estimate property prices based on relevant features.

8 features

Unnamed: 0string924 unique values
0 missing
Addressstring919 unique values
0 missing
Zipnominal834 unique values
0 missing
Pricenumeric226 unique values
4 missing
Areanumeric193 unique values
0 missing
Roomnumeric13 unique values
0 missing
Lonnumeric894 unique values
0 missing
Latnumeric886 unique values
0 missing

19 properties

924
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
4
Number of missing values in the dataset.
4
Number of instances with at least one value missing.
5
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.43
Percentage of instances having missing values.
0.05
Percentage of missing values.
Average class difference between consecutive instances.
62.5
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
12.5
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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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