Rahim Rasool committed Jan 24, 2019 1 ``````Data Science Dojo
`````` Rebecca Merrett committed Feb 10, 2020 2 ``````Copyright (c) 2019 - 2020 `````` Rahim Rasool committed Jan 24, 2019 3 4 5 `````` --- `````` Rahim Rasool committed Feb 05, 2019 6 ``````**Level:** Beginner
`````` Rahim Rasool committed Jan 24, 2019 7 8 9 10 11 12 13 14 15 16 17 18 ``````**Recommended Use:** Regression Models
**Domain:** Real Estate
## Real Estate Valuation Data Set ### Can you predict the price of a house? --- ![](310.jpg) --- `````` Rahim Rasool committed Feb 05, 2019 19 ``````This *beginner* level data set has 414 rows and 7 columns. `````` Rahim Rasool committed Jan 24, 2019 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 ``````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: --- ### Data Dictionary | 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 | ### Acknowledgement This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Real Estate Valuation Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set).
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* ``````