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README.md
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# Ames Housing
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This repository is a case study of applying various machine learning models to
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the problem of predicting house prices.
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This repository is a case study of applying various machine learning models
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to the problem of predicting house prices.
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The dataset is publicly available and can be downloaded, for example, at
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[Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques).
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The dataset is publicly available
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and can be downloaded, for example, at [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques).
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The case study is based on this [research paper](static/paper.pdf).
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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).
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### Table of Contents
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The analyses are presented in four notebooks that may be interactively worked
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with by following these links:
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- [Data Cleaning](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/01_data_cleaning.ipynb)
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- [Correlations](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/02_pairwise_correlations.ipynb)
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- [Visualizations](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/03_descriptive_visualizations.ipynb)
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- [Predictions](https://mybinder.org/v2/gh/webartifex/ames-housing/master?urlpath=lab/tree/04_predictive_models.ipynb)
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A video presentation of the case study is available on
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[YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=VSeGseoJsNA).
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- *Notebook 1*: [Data Cleaning](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/01_data_cleaning.ipynb)
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- *Notebook 2*: [Correlations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/02_pairwise_correlations.ipynb)
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- *Notebook 3*: [Visualizations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/03_descriptive_visualizations.ipynb)
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- *Notebook 4*: [Predictions](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/04_predictive_models.ipynb)
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## Installation
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### Objective
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The project can be cloned and may be worked with under the MIT open source
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license.
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Python 3.7 was used to prepare and test the provided code.
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Albeit the [poetry](https://python-poetry.org/) tool was used to manage the
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dependencies, a [requirements.txt](requirements.txt) file is also provided as
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an alternative.
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The **main goal** is to **show** students
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how **Python** can be used to solve a typical **data science** task.
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On a Unix system, run:
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- `git clone https://github.com/webartifex/ames-housing.git` (or use HTTPS
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instead)
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- either `poetry install` or `pip install -r requirements.txt` (in the latter
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case, it is suggested that a virtual environment be used)
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- after installation, `jupyter lab` opens a new tab in one's web browser where
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the notebooks and data files may be opened
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Alternatively, the project should also be runnable with the
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[Anaconda Distribution](https://www.anaconda.com/products/individual).
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### Prerequisites
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To be suitable for *beginners*, there are *no* formal prerequisites.
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It is only expected that the student has:
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- a *solid* understanding of the **English** language and
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- knowledge of **basic mathematics** from high school.
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Some background knowledge in Python is still helpful.
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To learn about Python and programming in detail,
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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.
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### Getting started & Installation
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To follow this workshop, an installation of **Python 3.8** or higher is expected.
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A popular and beginner friendly way is
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to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
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that not only ships Python itself
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but also comes pre-packaged with a lot of third-party libraries
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including [Python's scientific stack](https://scipy.org/about.html).
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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).
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## Contributing
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Feedback **is highly encouraged** and will be incorporated.
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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)
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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)
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if you are familiar with the concept.
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Simple issues that *anyone* can **help fix** are, for example,
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**spelling mistakes** or **broken links**.
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If you feel that some topic is missing entirely, you may also mention that.
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The materials here are considered a **permanent work-in-progress**.
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## About the Author
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Alexander Hess is a PhD student at the Chair of Logistics Management at the
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[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
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conducts research on urban delivery platforms and teaches an introductory
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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),
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[Spring Term 2020](https://vlv.whu.edu/campus/all/event.asp?objgguid=0x3354F4C108FF4E959CDD692A325D9AFE&from=vvz&gguid=0x262E29795DD742CFBDE72B12B69CEFD6&mode=own&lang=en&tguid=0x2E4A7D1FF3C34AD08FF07685461781C9)).
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Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
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Alexander Hess is a PhD student
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at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
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where he conducts research on urban delivery platforms
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and teaches coding courses based on Python in the BSc and MBA programs.
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Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).
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