Real Estate Valuation
Donated on 8/17/2018
The real estate valuation is a regression problem. The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan.
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Regression
Feature Type
Integer, Real
# Instances
414
# Features
6
Dataset Information
Additional Information
The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan. The “real estate valuation†is a regression problem. The data set was randomly split into the training data set (2/3 samples) and the testing data set (1/3 samples).
Has Missing Values?
No
Introductory Paper
By I. Yeh, Tzu-Kuang Hsu. 2018
Published in Applied Soft Computing
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| No | ID | Integer | no | ||
| X1 transaction date | Feature | Continuous | for example, 2013.250=2013 March, 2013.500=2013 June, etc. | no | |
| X2 house age | Feature | Continuous | year | no | |
| X3 distance to the nearest MRT station | Feature | Continuous | meter | no | |
| X4 number of convenience stores | Feature | Integer | number of convenience stores in the living circle on foot | integer | no |
| X5 latitude | Feature | Continuous | geographic coordinate, latitude | degree | no |
| X6 longitude | Feature | Continuous | geographic coordinate, longitude | degree | no |
| Y house price of unit area | Target | Continuous | 10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared | 10000 New Taiwan Dollar/Ping | no |
0 to 8 of 8
Additional Variable Information
The inputs are as follows X1=the transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.) X2=the house age (unit: year) X3=the distance to the nearest MRT station (unit: meter) X4=the number of convenience stores in the living circle on foot (integer) X5=the geographic coordinate, latitude. (unit: degree) X6=the geographic coordinate, longitude. (unit: degree) The output is as follow Y= house price of unit area (10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared)
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset real_estate_valuation = fetch_ucirepo(id=477) # data (as pandas dataframes) X = real_estate_valuation.data.features y = real_estate_valuation.data.targets # metadata print(real_estate_valuation.metadata) # variable information print(real_estate_valuation.variables)
Yeh,I-Cheng. (2018). Real Estate Valuation. UCI Machine Learning Repository. https://doi.org/10.24432/C5J30W.
@misc{misc_real_estate_valuation_477,
author = {Yeh,I-Cheng},
title = {{Real Estate Valuation}},
year = {2018},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5J30W}
}Creators
I-Cheng Yeh
140910@mail.tku.edu.tw
Department of Civil Engineering, Tamkang University
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.