https://github.com/vipul2001/housing-data-set-prediction
This is a solution to the kaggle competition on housing data set prediction
https://github.com/vipul2001/housing-data-set-prediction
Last synced: 2 months ago
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This is a solution to the kaggle competition on housing data set prediction
- Host: GitHub
- URL: https://github.com/vipul2001/housing-data-set-prediction
- Owner: vipul2001
- Created: 2019-08-16T13:44:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-08-16T13:58:06.000Z (over 5 years ago)
- Last Synced: 2025-01-17T06:44:46.357Z (4 months ago)
- Language: Jupyter Notebook
- Size: 263 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Housing-data-set-prediction
This is a solution to the kaggle competition on housing data set prediction
# Competition Description
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
# Practice Skills
Creative feature engineering
Advanced regression techniques like random forest and gradient boosting
# Acknowledgments
The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.