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Simplified Property Prediction Dataset
Data Science and Analytics
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About
housing-related metrics is presented here, designed primarily to enhance machine learning regression abilities. It includes essential features associated with properties, allowing users to practise and develop skills in predicting continuous outcomes, such as house prices per unit area. The data is structured using integer types, making it immediately suitable for various regression models.
Columns
The dataset includes the following columns, all of which are of integer type:
- Transaction date: The date when the property transaction occurred.
- House Age: The age of the house at the time of the transaction.
- Distance from nearest Metro station (km): The proximity of the property to the closest metro station, measured in kilometres.
- Number of convenience stores: The count of convenience stores located nearby.
- latitude: The geographical latitude coordinate of the property.
- longitude: The geographical longitude coordinate of the property.
- Number of bedrooms: The total number of bedrooms in the house.
- House size (sqft): The size of the house, expressed in square feet.
- House price of unit area: The price of the house per unit area.
Distribution
The dataset is typically provided in a CSV format. It contains 9 columns and consists of 414 individual records or rows, without any missing or mismatched entries for any of the attributes. The file size is approximately 22.71 kB. All data points within the columns are integers, facilitating direct use in numerical models.
Usage
This dataset is an excellent resource for anyone looking to improve their machine learning regression skills. It is ideally suited for:
- Developing and testing various regression algorithms.
- Academic projects and assignments focused on predictive modelling.
- Exploratory data analysis to understand factors influencing housing prices.
- Educational purposes for teaching core concepts of machine learning and data science.
Coverage
The geographic scope of this dataset is focused on India, with specific latitude values generally ranging between 24.9 and 25, and longitude values between 121 and 122. The temporal coverage for transactions spans from approximately 2012.67 to 2013.58. The data does not contain specific demographic information, but focuses on property characteristics and location-based features. There are no gaps or missing data points within the 414 records provided for each column.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
Intended users for this dataset include:
- Machine learning engineers and data scientists: For building and refining regression models.
- Students and educators: As a practical tool for learning and teaching about regression in machine learning.
- Researchers: For studies related to urban planning, real estate markets, or factors affecting property valuation.
- Analysts: Seeking to understand property trends and influences in a simplified setting.
Dataset Name Suggestions
- Housing Price Regression Data
- Simplified Property Prediction Dataset
- Metro Housing Factors
- Residential Regression Dataset
- India Housing Features
Attributes
Original Data Source: Simplified Property Prediction Dataset
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