diff --git a/README.md b/README.md index 58ace5e..dbf207d 100644 --- a/README.md +++ b/README.md @@ -1,52 +1,74 @@ # 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 ](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 ](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 ](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 ](https://github.com/webartifex/intro-to-python#installation). + + +## Contributing + +Feedback **is highly encouraged** and will be incorporated. +Open an issue in the [issues tracker ](https://github.com/webartifex/ames-housing/issues) + or initiate a [pull request ](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). diff --git a/static/link/README.md b/static/link/README.md new file mode 100644 index 0000000..f5c9136 --- /dev/null +++ b/static/link/README.md @@ -0,0 +1,2 @@ +This folder contains small images +that are used to enhance the links in the notebooks and markdown files. diff --git a/static/link/to_gh.png b/static/link/to_gh.png new file mode 100644 index 0000000..01f1a8a Binary files /dev/null and b/static/link/to_gh.png differ