Predict sales prices and practice feature engineering, RFs, and gradient boosting
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地址：https://www.kaggle.com/c/house-prices-advanced-regression-techniques
| 翻译项目原地址 | 作者 | 本库地址 | 翻译后版本 |
|---|---|---|---|
| COMPREHENSIVE DATA EXPLORATION WITH PYTHON | Pedro Marcelino | GO | GO |
| --- | --- | --- | --- |
Start here if... You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.
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.