Data
New-Delhi-Rental-Listings

New-Delhi-Rental-Listings

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Context The dataset is from a rental price prediction project I did. Includes different types of properties (Apartments, Independent floors, Independent houses, Villas etc.) It contains 12000 rental listings from a popular real estate website. It can be used for rental prediction projects, analysis of areas of affluence etc. Content The dataset multiple quantitative, categorical and co-ordinate features including : Data about the houses : sizesqft, propertyType, bedrooms, Data about the locality of the house : latitude, longitude, localityName, suburbName, cityName, Asking Rent : price, Property agency : companyName, Distance to closest landmarks (geodesic distance, not driving-road distance) : closestmterostationkm, APdistkm (Indira Gandhi International Airport), Aiimsdistkm (All India Institute of Medical Science - major government hospital), NDRLWdist_km (New Delhi Railway Station), Heatmap of Data Red Vmax for monthly rent of Rs. 2lakh/mo and above.

15 features

Unnamed:_0numeric17890 unique values
0 missing
size_sq_ftnumeric700 unique values
0 missing
propertyTypestring4 unique values
0 missing
bedroomsnumeric12 unique values
0 missing
latitudenumeric8767 unique values
0 missing
longitudenumeric7651 unique values
0 missing
localityNamestring781 unique values
0 missing
suburbNamestring12 unique values
0 missing
cityNamestring1 unique values
0 missing
pricenumeric432 unique values
0 missing
companyNamestring1387 unique values
0 missing
closest_mtero_station_kmnumeric9765 unique values
0 missing
AP_dist_kmnumeric9765 unique values
0 missing
Aiims_dist_kmnumeric9765 unique values
0 missing
NDRLW_dist_kmnumeric9765 unique values
0 missing

19 properties

17890
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
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.
10
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
66.67
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.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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