Merge branch 'refurbish-project' into develop

LICENSE.txt

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2018-2020 Alexander Hess [alexander@webartifex.biz]
+Copyright (c) 2018-2021 Alexander Hess [alexander@webartifex.biz]
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,52 +1,80 @@
 # 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](paper.pdf).
+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="link_to_yt.png">](https://www.youtube.com/watch?v=VSeGseoJsNA).
+- *Notebook 0*: [Data Cleaning](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/00_data_cleaning.ipynb)
+- *Notebook 1*: [Correlations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/01_pairwise_correlations.ipynb)
+- *Notebook 2*: [Visualizations](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/02_descriptive_visualizations.ipynb)
+- *Notebook 3*: [Predictions](https://mybinder.org/v2/gh/webartifex/ames-housing/main?urlpath=lab/tree/03_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).
+
+As this project assumes a couple of third-party packages
+    that are *not* part of the Anaconda Distribution,
+    it is most likely necessary
+    to run the command `pip install -r requirements.txt`
+    before working with the notebook files.
+
+
+## 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).

data/data_clean_with_transformations_and_factors.csv

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@@ -700,7 +699,6 @@ Order,PID,1st Flr SF,1st Flr SF (box-cox-0),2nd Flr SF,3Ssn Porch,Bedroom AbvGr,
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+1773,528366050,3228.0,8.07961802938984,0.0,0.0,4,TA,No,1,0,Gd,1969.0,1231.0,0.0,GLQ,Unf,SBrkr,291.0,NA,Gd,1,3,Typ,546.0,2,TA,RFn,TA,3228.0,8.07961802938984,0,1,Gd,Gtl,12692.0,15.724859195735945,IR1,0.0,0.0,0.0,5,75.0,5,8,Y,0.0,NA,0.0,10,4.0,3200.0,630.0,6428.0,23.879201299438567,AllPub,264.0,0,1,1,0,0,0,0,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,15,14,430000.0,12.971540487669746
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@@ -2621,6 +2620,7 @@ Order,PID,1st Flr SF,1st Flr SF (box-cox-0),2nd Flr SF,3Ssn Porch,Bedroom AbvGr,
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+2667,902400110,1518.0,7.325148957955575,1518.0,0.0,4,TA,Mn,0,0,TA,1107.0,0.0,0.0,Unf,Unf,SBrkr,0.0,GdPrv,TA,2,2,Typ,840.0,3,TA,Unf,Ex,3608.0,8.190908881182514,1,1,Ex,Gtl,22950.0,17.294696462432224,IR2,572.0,0.0,0.0,6,260.0,9,10,Y,0.0,NA,410.0,12,2.5,1107.0,670.0,4715.0,22.143531063375193,AllPub,0.0,0,1,1,0,0,1,0,0,1,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,114,13,475000.0,13.071070083016778
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pyproject.toml

@@ -1,14 +1,15 @@
 [build-system]
-build-backend = "poetry.masonry.api"
-requires = ["poetry>=0.12"]
-
-[tool.black]
-line-length = 79
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-authors = ["Alexander Hess <alexander@webartifex.biz>"]
+name = "ames-housing"
+version = "0.1.0.dev0"
+
+authors = [
+    "Alexander Hess <alexander@webartifex.biz>",
+]
 description = "A case study on predicting house prices in Ames, Iowa"
-homepage = "https://github.com/webartifex/ames-housing"
 keywords = [
     "data-science",
     "data-cleaning",
@@ -18,27 +19,26 @@ keywords = [
     "predictive-analytics",
 ]
 license = "MIT"
-name = "ames-housing"
+
 readme = "README.md"
+homepage = "https://github.com/webartifex/ames-housing"
 repository = "https://github.com/webartifex/ames-housing"
-version = "0.1.0"
 
 [tool.poetry.dependencies]
-jupyterlab = "^2.1.5"
-matplotlib = "^3.2.2"
+python = "^3.8"
+jupyterlab = "^3.0.16"
 missingno = "^0.4.2"
-numpy = "^1.19.0"
-pandas = "^1.0.5"
-python = "^3.7"
-requests = "^2.24.0"
-seaborn = "^0.10.1"
-sklearn = "^0.0"
-statsmodels = "^0.11.1"
-tabulate = "^0.8.7"
-tqdm = "^4.47.0"
-xlrd = "^1.2.0"
-xlwt = "^1.3.0"
+matplotlib = "^3.4.2"
+numpy = "^1.20.3"
+pandas = "^1.2.4"
+requests = "^2.25.1"
+scikit-learn = "^0.24.2"
+seaborn = "^0.11.1"
+statsmodels = "^0.12.2"
+tabulate = "^0.8.9"
+tqdm = "^4.61.0"
+xlrd = "^2.0.1"
 
 [tool.poetry.dev-dependencies]
-black = "^19.10b0"
-pylint = "^2.5.3"
+black = "^21.5b1"
+pylint = "^2.8.2"

requirements.txt

@@ -1,82 +1,85 @@
-appdirs==1.4.4
-astroid==2.4.2
-attrs==19.3.0
-backcall==0.2.0
-black==19.10b0
-bleach==3.1.5
-certifi==2020.6.20
-chardet==3.0.4
-click==7.1.2
-cycler==0.10.0
-decorator==4.4.2
-defusedxml==0.6.0
-entrypoints==0.3
-idna==2.10
-importlib-metadata==1.7.0
-ipykernel==5.3.0
-ipython==7.16.1
-ipython-genutils==0.2.0
-isort==4.3.21
-jedi==0.17.1
-Jinja2==2.11.2
-joblib==0.15.1
-json5==0.9.5
-jsonschema==3.2.0
-jupyter-client==6.1.3
-jupyter-core==4.6.3
-jupyterlab==2.1.5
-jupyterlab-server==1.1.5
-kiwisolver==1.2.0
-lazy-object-proxy==1.4.3
-MarkupSafe==1.1.1
-matplotlib==3.2.2
-mccabe==0.6.1
+anyio==3.1.0; python_full_version >= "3.6.2" and python_version >= "3.6"
+appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin"
+argon2-cffi==20.1.0; python_version >= "3.6"
+async-generator==1.10; python_full_version >= "3.6.1" and python_version >= "3.6"
+attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+babel==2.9.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+backcall==0.2.0; python_version >= "3.7"
+bleach==3.3.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+certifi==2020.12.5; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+cffi==1.14.5; implementation_name == "pypy" and python_version >= "3.6"
+chardet==4.0.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0"
+cycler==0.10.0; python_version >= "3.7"
+decorator==5.0.9; python_version >= "3.7"
+defusedxml==0.7.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+entrypoints==0.3; python_version >= "3.6"
+idna==2.10; python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6")
+ipykernel==5.5.5; python_version >= "3.6"
+ipython-genutils==0.2.0; python_version >= "3.7"
+ipython==7.23.1; python_version >= "3.7"
+jedi==0.18.0; python_version >= "3.7"
+jinja2==3.0.1; python_version >= "3.6"
+joblib==1.0.1; python_version >= "3.6"
+json5==0.9.5; python_version >= "3.6"
+jsonschema==3.2.0; python_version >= "3.6"
+jupyter-client==6.2.0; python_full_version >= "3.6.1" and python_version >= "3.6"
+jupyter-core==4.7.1; python_full_version >= "3.6.1" and python_version >= "3.6"
+jupyter-server==1.8.0; python_version >= "3.6"
+jupyterlab-pygments==0.1.2; python_version >= "3.6"
+jupyterlab-server==2.5.2; python_version >= "3.6"
+jupyterlab==3.0.16; python_version >= "3.6"
+kiwisolver==1.3.1; python_version >= "3.7"
+markupsafe==2.0.1; python_version >= "3.6"
+matplotlib-inline==0.1.2; python_version >= "3.7"
+matplotlib==3.4.2; python_version >= "3.7"
 missingno==0.4.2
-mistune==0.8.4
-nbconvert==5.6.1
-nbformat==5.0.7
-notebook==6.0.3
-numpy==1.19.0
-packaging==20.4
-pandas==1.0.5
-pandocfilters==1.4.2
-parso==0.7.0
-pathspec==0.8.0
-patsy==0.5.1
-pexpect==4.8.0
-pickleshare==0.7.5
-prometheus-client==0.8.0
-prompt-toolkit==3.0.5
-ptyprocess==0.6.0
-Pygments==2.6.1
-pylint==2.5.3
-pyparsing==2.4.7
-pyrsistent==0.16.0
-python-dateutil==2.8.1
-pytz==2020.1
-pyzmq==19.0.1
-regex==2020.6.8
-requests==2.24.0
-scikit-learn==0.23.1
-scipy==1.5.0
-seaborn==0.10.1
-Send2Trash==1.5.0
-six==1.15.0
-sklearn==0.0
-statsmodels==0.11.1
-tabulate==0.8.7
-terminado==0.8.3
-testpath==0.4.4
-threadpoolctl==2.1.0
-toml==0.10.1
-tornado==6.0.4
-tqdm==4.47.0
-traitlets==4.3.3
-typed-ast==1.4.1
-urllib3==1.25.9
-wcwidth==0.2.5
-webencodings==0.5.1
-wrapt==1.12.1
-xlrd==1.2.0
-xlwt==1.3.0
-zipp==3.1.0
+mistune==0.8.4; python_version >= "3.6"
+nbclassic==0.3.1; python_version >= "3.6"
+nbclient==0.5.3; python_full_version >= "3.6.1" and python_version >= "3.6"
+nbconvert==6.0.7; python_version >= "3.6"
+nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.6"
+nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.6"
+notebook==6.4.0; python_version >= "3.6"
+numpy==1.20.3; python_version >= "3.7"
+packaging==20.9; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+pandas==1.2.4; python_full_version >= "3.7.1"
+pandocfilters==1.4.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+parso==0.8.2; python_version >= "3.7"
+patsy==0.5.1; python_version >= "3.6"
+pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7"
+pickleshare==0.7.5; python_version >= "3.7"
+pillow==8.2.0; python_version >= "3.7"
+prometheus-client==0.10.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+prompt-toolkit==3.0.18; python_full_version >= "3.6.1" and python_version >= "3.7"
+ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.7" and os_name != "nt"
+py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.6" and python_full_version >= "3.4.0"
+pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+pygments==2.9.0; python_version >= "3.7"
+pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
+pyrsistent==0.17.3; python_version >= "3.6"
+python-dateutil==2.8.1; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7")
+pytz==2021.1; python_full_version >= "3.7.1" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
+pywin32==300; sys_platform == "win32" and python_version >= "3.6"
+pywinpty==1.1.1; os_name == "nt" and python_version >= "3.6"
+pyzmq==22.0.3; python_full_version >= "3.6.1" and python_version >= "3.6"
+requests==2.25.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
+scikit-learn==0.24.2; python_version >= "3.6"
+scipy==1.6.1; python_version >= "3.7"
+seaborn==0.11.1; python_version >= "3.6"
+send2trash==1.5.0; python_version >= "3.6"
+six==1.16.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+sniffio==1.2.0; python_full_version >= "3.6.2" and python_version >= "3.6"
+statsmodels==0.12.2; python_version >= "3.6"
+tabulate==0.8.9
+terminado==0.10.0; python_version >= "3.6"
+testpath==0.5.0; python_version >= "3.6"
+threadpoolctl==2.1.0; python_version >= "3.6"
+tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.6"
+tqdm==4.61.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
+traitlets==5.0.5; python_full_version >= "3.6.1" and python_version >= "3.7"
+urllib3==1.26.4; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6"
+wcwidth==0.2.5; python_full_version >= "3.6.1" and python_version >= "3.7"
+webencodings==0.5.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
+websocket-client==1.0.1; python_version >= "3.6"
+xlrd==2.0.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")

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.