Cleanup styling "docs: Updated 179 broken links"

CONTRIBUTING.md

@@ -1,45 +1,45 @@
 ## How to contribute to Pandas-Profiling
 
-Pandas-profiling aims to ease exploratory data analysis for structured datasets, including time-series.
+Pandas-profiling aims to ease exploratory data analysis for structured datasets, including time-series. 
 Our focus is to provide users with useful and robust statistics for such datasets encountered in industry, academia and elsewhere.
 Pandas-profiling is open-source and stimulates contributions from passionate community users.
 
-#### Themes to contribute
 
+#### Themes to contribute
 In line with our aim, we identify the following themes:
 
-- **Exploratory data analysis**:
+- **Exploratory data analysis**: 
   The core of the package is a dataset summarization by its main characteristics, which is complemented with warnings on data issues and visualisations.
 
-  _Suggestions for contribution_:
+  _Suggestions for contribution_: 
   Extend the support of more data types (think of paths, location or GPS coordinates and ordinal data types),
-  text data (e.g. encoding, vocabulary size, spelling errors, language detection),
-  time series analysis,
+  text data (e.g. encoding, vocabulary size, spelling errors, language detection), 
+  time series analysis, 
   or even images (e.g. dimensions, EXIF).
-
+  
   _Related_: [#7][i7], [#129][i129], [#190][i190], [#204][i204] or [create one](https://github.com/ydataai/ydata-profiling/issues/new/choose).
 
-- **Stability, Performance and Restricted environment compatibility:**
+- **Stability, Performance and Restricted environment compatibility:** 
   Data exploration takes place in all kinds of conditions, on the latest machine learning platforms with enormous dataset to managed environments in large corporations.
   `pandas-profiling` helps analysts, researchers and engineers alike in these cases.
   We do this by fixing bugs, improving performance on big datasets and adding environment compatibility.
-
-  _Suggestions for contribution (Performance)_:
-  Perform concurrency analysis or profile execution times and leverage the gained insights for improved performance (e.g. multiprocessing, cython, numba) or test the performance of `pandas-profiling` with [big data sets](https://www.stats.govt.nz/large-datasets/csv-files-for-download/) and corresponding commonly used data formats (such as parquet).
-
-  _Suggestions for contribution (Stability)_:
+  
+  _Suggestions for contribution (Performance)_: 
+  Perform concurrency analysis or profile execution times and leverage the gained insights for improved performance (e.g. multiprocessing, cython, numba) or test the performance of `pandas-profiling` with [big data sets](https://www.stats.govt.nz/large-datasets/csv-files-for-download/) and corresponding commonly used data formats (such as parquet). 
+  
+  _Suggestions for contribution (Stability)_: 
   Either review the code and add tests or watch the [issues page](https://github.com/ydataai/ydata-profiling/issues) and [Stackoverflow tag](https://stackoverflow.com/questions/tagged/pandas-profiling) to find current issues.
-
+     
   _Related_: [#98][i98], [#122][i122] or [create one](https://github.com/ydataai/ydata-profiling/issues/new/choose).
 
-- **Interaction, presentation and user experience**:
+- **Interaction, presentation and user experience**: 
   As `pandas-profiling` eases exploratory data analysis, working with the package should reflect that.
   Interaction and user experience plays a central role in working with the package.
   Working on interactive and static features is possible through the modular nature of the package: the user can configure which features to use.
 
   _Suggestions for contribution (interactivity)_:
-  Interactivity allows for more user friendly applications, including but not limited to on demand analysis (don't compute what you don't want to see) and interactive histograms and correlations.
-  This is ideal for smaller datasets, where we can compute this on-the-fly.
+  Interactivity allows for more user friendly applications, including but not limited to on demand analysis (don't compute what you don't want to see) and interactive histograms and correlations. 
+  This is ideal for smaller datasets, where we can compute this on-the-fly. 
   `ipywidgets` would be a great place to start (e.g. [widget based view](https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20List.html)).
 
   _Suggestions for contribution (presentation)_:
@@ -48,17 +48,17 @@ In line with our aim, we identify the following themes:
 
   _Related_: [#161][i161], [#175][i175], [#191][i191] or [create one](https://github.com/ydataai/ydata-profiling/issues/new/choose).
 
-- **Community**:
+- **Community**: 
   The success of this package demonstrates the power of sharing and working together.
   You are welcome as part of this community.
-
+  
   _Suggestions for contribution_:
   Share with us if this package is of value to you, let us know [in our community](https://discord.com/invite/mw7xjJ7b7s).
   We are interested in how you use `pandas-profiling` in your work.
-
+  
   _Related_: [#87][i87] or [create one](https://github.com/ydataai/ydata-profiling/issues/new/choose).
 
-- **Machine learning:**
+- **Machine learning:** 
   `pandas-profiling` is not a machine learning package, even though many of our users use EDA as a step prior to developing their models.
   Our focus lies in the exploratory data analysis.
   Any functionality that enables machine learning applications by more effective data profiling, is welcome.
@@ -67,17 +67,18 @@ In line with our aim, we identify the following themes:
 
 #### **Did you find a bug?**
 
-- **Ensure the bug was not already reported** by searching on Github under [Issues](https://github.com/ydataai/ydata-profiling/issues).
+* **Ensure the bug was not already reported** by searching on Github under [Issues](https://github.com/ydataai/ydata-profiling/issues).
 
-- If you're unable to find an open issue addressing the problem, [open a new one](https://github.com/ydataai/ydata-profiling/issues/new/choose).
-  If possible, use the relevant bug report templates to create the issue.
+* If you're unable to find an open issue addressing the problem, [open a new one](https://github.com/ydataai/ydata-profiling/issues/new/choose). 
+If possible, use the relevant bug report templates to create the issue. 
 
 #### **Did you write a patch that fixes a bug?**
 
-- Open a new Github pull request with the patch.
+* Open a new Github pull request with the patch.
+
+* Ensure the PR description clearly describes the problem and solution. 
+Include the relevant issue number if applicable.
 
-- Ensure the PR description clearly describes the problem and solution.
-  Include the relevant issue number if applicable.
 
 #### Acknowledgements
 
@@ -98,3 +99,4 @@ See the [Contributor Graph](https://github.com/ydataai/ydata-profiling/graphs/co
 [i161]: https://github.com/ydataai/ydata-profiling/issues/161
 [i175]: https://github.com/ydataai/ydata-profiling/issues/175
 [i191]: https://github.com/ydataai/ydata-profiling/issues/191
+