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Ames Housing Zero RMSE Data
Data Science and Analytics
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About
This dataset serves as a 'solution' file for the popular House Prices: Advanced Regression Techniques competition found on Kaggle. Its core purpose is to facilitate offline reproduction and experimentation with advanced regression models, thereby circumventing the limitations and pressures of public leaderboard submissions. The original underlying data, known as the Ames Housing Dataset, was first published by Dean De Cock in 2011. This specific solution file allows users to precisely evaluate the Root-Mean-Squared-Error (RMSE) between their model's predictions and the provided 'perfect' solution, which is designed to yield a public leaderboard score of 0.00000. It is an ideal resource for refining skills in areas such as machine learning pipelines, extensive hyper-parameter tuning, and comparative analysis of various estimators.
Columns
- Id: This column represents a unique identifier for each house record within the dataset. It typically ranges from 1461 to 2919, with a mean value of approximately 2190. There are 1461 valid entries for this identifier.
- SalePrice: This is the target variable for the regression task, denoting the sale price of a given house. Its values generally span from about 12.8 thousand to 615 thousand, with a mean of around 180 thousand. The column contains 1459 valid entries.
Distribution
The dataset is made available as a
submission.csv file. It has a file size of 20.32 kB and is structured with 2 distinct columns. The dataset comprises approximately 1460 records, providing the 'solution' data for the house price prediction challenge. All data points across both columns are validated, with no identified mismatched or missing values, ensuring data integrity for analysis.Usage
This dataset is particularly well-suited for several applications:
- Offline Competition Simulation: Enabling users to re-run the Kaggle House Prices: Advanced Regression Techniques competition in a local environment.
- Advanced Model Development: Facilitating the testing and refinement of sophisticated machine learning pipelines, including the comparison of different regression estimators and the execution of extensive hyper-parameter tuning exercises.
- Performance Benchmarking: Serving as a precise tool for calculating and validating the Root-Mean-Squared-Error (RMSE) of regression models against a known, zero-error baseline.
- Educational Training: Providing a practical and robust environment for students and practitioners to learn and apply advanced regression analysis techniques without external submission constraints.
Coverage
The original data from which this solution file is derived pertains to housing transactions in Ames, Iowa, United States. While the specific time span of this solution file is not detailed, the initial dataset upon which the Kaggle competition is based originates from a publication in 2011.
License
CC0: Public Domain
Who Can Use It
This dataset offers significant value to a range of users:
- Data Scientists: For developing, testing, and refining their regression models.
- Machine Learning Practitioners: To experiment with advanced techniques and optimise model performance without public exposure.
- Students: As a valuable resource for understanding and applying core and advanced regression analysis principles.
- Researchers: For benchmarking novel algorithms and methodologies against a well-established and widely recognised problem.
Dataset Name Suggestions
- House Prices: Offline Regression Solution
- Ames Housing Zero RMSE Data
- Kaggle House Prices Training Solution
- Advanced Regression Techniques Practice Data
- House Price Prediction Benchmark
Attributes
Original Data Source: Ames Housing Zero RMSE Data
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