| data | ||
| .gitignore | ||
| 1_data_cleaning.ipynb | ||
| 2_pairwise_correlations.ipynb | ||
| 3_descriptive_visualizations.ipynb | ||
| 4_predictive_models.ipynb | ||
| LICENSE | ||
| map.pdf | ||
| paper.pdf | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
| requirements.txt | ||
| utils.py | ||
Ames Housing
This repository is a case study of applying various machine learning models to the problem of predicting house prices.
The dataset is publicly available and can be downloaded, for example, at Kaggle.
The case study is based on this research paper.
The analyses are presented in four notebooks that may be interactively worked with by following these links:
Installation
The project can be cloned and may be worked with under the MIT open source license. Python 3.7 was used to prepare and test the provided code. Albeit the poetry tool was used to manage the dependencies, a requirements.txt file is also provided as an alternative.
On a Unix system, run:
git clone https://github.com/webartifex/ames-housing.git(or use HTTPS instead)- either
poetry installorpip install -r requirements.txt(in the latter case, it is suggested that a virtual environment be used) - after installation,
jupyter labopens a new tab in one's web browser where the notebooks and data files may be opened
Alternatively, the project should also be runnable with the Anaconda Distribution.
About the Author
Alexander Hess is a PhD student at the Chair of Logistics Management at the WHU - Otto Beisheim School of Management where he conducts research on urban delivery platforms and teaches an introductory course on Python (cf., Fall Term 2019, Spring Term 2020).
Connect him on LinkedIn.