# Kaggle-House-Prices
Predict sales prices and practice feature engineering, RFs, and gradient boosting
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
## 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.
