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# Ames Housing
This repository is a case study of applying various machine learning models to
the problem of predicting house prices.
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](https://www.kaggle.com/c/house-prices-advanced-regression-techniques).
The dataset is publicly available
and can be downloaded, for example, at [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques).
The case study is based on this [research paper](static/paper.pdf).
A video presentation of the case study is available on [YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=VSeGseoJsNA).
### Table of Contents
The analyses are presented in four notebooks that may be interactively worked
with by following these links:
- [Data Cleaning](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/01_data_cleaning.ipynb)
- [Correlations](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/02_pairwise_correlations.ipynb)
- [Visualizations](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/03_descriptive_visualizations.ipynb)
- [Predictions](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/04_predictive_models.ipynb)
A video presentation of the case study is available on
[YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=VSeGseoJsNA).
- *Notebook 1*: [Data Cleaning](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/01_data_cleaning.ipynb)
- *Notebook 2*: [Correlations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/02_pairwise_correlations.ipynb)
- *Notebook 3*: [Visualizations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/03_descriptive_visualizations.ipynb)
- *Notebook 4*: [Predictions](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/04_predictive_models.ipynb)
## Installation
### Objective
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](https://python-poetry.org/) tool was used to manage the
dependencies, a [requirements.txt](requirements.txt) file is also provided as
an alternative.
The **main goal** is to **show** students
how **Python** can be used to solve a typical **data science** task.
On a Unix system, run:
- `git clone https://github.com/webartifex/ames-housing.git` (or use HTTPS
instead)
- either `poetry install` or `pip install -r requirements.txt` (in the latter
case, it is suggested that a virtual environment be used)
- after installation, `jupyter lab` opens 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](https://www.anaconda.com/products/individual).
### Prerequisites
To be suitable for *beginners*, there are *no* formal prerequisites.
It is only expected that the student has:
- a *solid* understanding of the **English** language and
- knowledge of **basic mathematics** from high school.
Some background knowledge in Python is still helpful.
To learn about Python and programming in detail,
this [introductory course <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python) is recommended.
### Getting started & Installation
To follow this workshop, an installation of **Python 3.8** or higher is expected.
A popular and beginner friendly way is
to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
that not only ships Python itself
but also comes pre-packaged with a lot of third-party libraries
including [Python's scientific stack](https://scipy.org/about.html).
Detailed instructions can be found [here <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python#installation).
## Contributing
Feedback **is highly encouraged** and will be incorporated.
Open an issue in the [issues tracker <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/ames-housing/issues)
or initiate a [pull request <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://help.github.com/en/articles/about-pull-requests)
if you are familiar with the concept.
Simple issues that *anyone* can **help fix** are, for example,
**spelling mistakes** or **broken links**.
If you feel that some topic is missing entirely, you may also mention that.
The materials here are considered a **permanent work-in-progress**.
## About the Author
Alexander Hess is a PhD student at the Chair of Logistics Management at the
[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
conducts research on urban delivery platforms and teaches an introductory
course on Python (cf., [Fall Term 2019](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en),
[Spring Term 2020](https://vlv.whu.edu/campus/all/event.asp?objgguid=0x3354F4C108FF4E959CDD692A325D9AFE&from=vvz&gguid=0x262E29795DD742CFBDE72B12B69CEFD6&mode=own&lang=en&tguid=0x2E4A7D1FF3C34AD08FF07685461781C9)).
Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
Alexander Hess is a PhD student
at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
where he conducts research on urban delivery platforms
and teaches coding courses based on Python in the BSc and MBA programs.
Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).

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