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.
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.
Competition URL: Housing Prices Competition for Kaggle Learn Users
Notebook URL: Predicting House Prices
- Python version 3.9 or above;
- Anaconda and Python DataScience Stack (pandas, numpy and matplotlib in particular);
- Jupyter Notebook
Email: csfelix08@gmail.com
Linkedin: csfelix
Instagram: csfelix08
Portfolio: CSFelix.io
Kaggle: DSFelix