Recommended Use: Regression Models
Domain: Real Estate
This data set has 414 rows and 7 columns. It provides the market historical data set of real estate valuations which are collected from Sindian Dist., New Taipei City, Taiwan. This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, and regression modelling techniques. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set:
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|---|---|---|---|---|---|
| 1 | X1 transaction date | The transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.) | Qualitative | 2013.500, 2013.500, 2013.333 | 0 |
| 2 | X2 house age | The house age (unit: year) | Quantitative | 19.5, 13.3, 5.0 | 0 |
| 3 | X3 distance to the nearest MRT station | The distance to the nearest MRT station (unit: meter) | Quantitative | 390.5684, 405.21340, 23.38284 | 0 |
| 4 | X4 number of convenience stores | The number of convenience stores in the living circle on foot | Quantitative | 6, 8, 1 | 0 |
| 5 | X5 latitude | The geographic coordinate, latitude (unit: degree) | Quantitative | 24.97937, 24.97544, 24.94925 | 0 |
| 6 | X6 longtitude | The geographic coordinate, longitude (unit: degree) | Quantitative | 121.54243, 121.49587, 121.51151 | 0 |
| 7 | Y house price of unit area | The house price of unit area (10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared) for example, 29.3 = 293,000 New Taiwan Dollar/Ping | Quantitative | 29.3, 33.6, 47.7 | 0 |
This data set has been sourced from the Machine Learning Repository of University of California, Irvine Real Estate Valuation Data Set (UC Irvine).
The UCI page mentions the following as the original source of the data set:
Yeh, I. C., & Hsu, T. K. (2018). Building real estate valuation models with comparative approach through case-based reasoning. Applied Soft Computing, 65, 260-271