Rahim Rasool committed Jan 11, 2019 1 2 3 4 5 ``````Data Science Dojo
Copyright (c) 2016 - 2019 --- `````` Rahim Rasool committed Jan 11, 2019 6 ``````**Level** Intermediate
`````` Rahim Rasool committed Jan 11, 2019 7 8 9 10 11 12 13 14 15 16 17 18 ``````**Recommended Use:** Regression Models
## Real Estate Valuation Data Set ### Can you predict the price of a house? --- ![](310.jpg) --- `````` Rahim Rasool committed Jan 11, 2019 19 20 21 22 23 ``````This *intermediate* level 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: `````` Rahim Rasool committed Jan 11, 2019 24 25 26 27 28 `````` --- ### Data Dictionary `````` Rahim Rasool committed Jan 11, 2019 29 30 31 32 33 34 35 36 37 ``````| 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 | `````` Rahim Rasool committed Jan 11, 2019 38 39 40 41 `````` ### Acknowledgement `````` Rahim Rasool committed Jan 11, 2019 42 ``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 ``