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diff --git a/data/README.md b/data/README.md
@@ -96,45 +96,7 @@ See [Data Generation](./data-generation.md#data-generation)
 
 ## Post model-creation analysis, ML interpretation/explainability
 
-### Libraries & packages
-
-- [Yellowbrick](https://www.scikit-yb.org/en/latest/#yellowbrick-machine-learning-visualization) - is a suite of visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process
-- [Shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model
-- [LIME](https://github.com/marcotcr/lime)
-- [4 Python Libraries For Getting Better Model Interpretability](https://www.analyticsindiamag.com/4-python-libraries-for-getting-better-model-interpretability/)
-- [Integrated Gradients: Axiomatic Attribution for Deep Networks](https://github.com/ankurtaly/Integrated-Gradients) | [Paper](https://arxiv.org/abs/1703.01365)
-- [Resources on GitHub on interpretability](https://github.com/topics/interpretability)
-- [Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) - A Curated, but Probably Biased and Incomplete, List of Awesome Machine Learning Interpretability Resources
-
-### Articles, blog posts, papers, notebooks, books, presentations
-
-- [DataRobot: Model Interpretability - What is Model Interpretability in Machine Learning?](https://www.datarobot.com/wiki/interpretability/)
-- [Model Interpretability with SHAP](http://www.f1-predictor.com/model-interpretability-with-shap/)
-- [Interpreting bag of words models with SHAP](https://sararobinson.dev/2019/04/23/interpret-bag-of-words-models-shap.html)
-- [Explain any machine learning model prediction - using SHAP](https://towardsdatascience.com/how-to-explain-any-machine-learning-model-prediction-30654b0c1c8)
-- [Explain ML Models notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/explain-model)
-- [How to explain the prediction of a ML model](https://lilianweng.github.io/lil-log/2017/08/01/how-to-explain-the-prediction-of-a-machine-learning-model.html)
-- [Explaining complex machine learning models with LIME](https://datascienceplus.com/explaining-complex-machine-learning-models-with-lime/)
-- Hermeneutic Investigations: ML Interpreation - why?: [Video](https://www.youtube.com/watch?v=pmdYlahqA_g) | [Slides](https://github.com/daplantagenet/iml.github.io/blob/master/Hermeneutic%20Investigations.pdf) by [Dean Allsopp](http://github.com/daplantagenet)
-- [Explaining Explanations: An Overview ofInterpretability of Machine Learning](https://arxiv.org/pdf/1806.00069.pdf)
-- [Explaining Black-Box Machine Learning Models](https://shirinsplayground.netlify.com/2018/07/explaining_ml_models_code_caret_iml/)
-- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
-- [R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari: Model interpretability](https://www.oreilly.com/library/view/r-machine-learning/9781789807943/dcd398be-3488-423c-942c-69d1eac253f5.xhtml)
-- [Hands-on Machine Learning with R: Interpretable Machine Learning](https://bradleyboehmke.github.io/HOML/iml.html)
-- Tree SHAP
-  - [Consistent Individualized Feature Attribution for Tree Ensembles ](https://arxiv.org/abs/1802.03888)
-  - [Consistent feature attribution for tree ensembles](https://arxiv.org/abs/1706.06060)
-- [Exact SHAP: A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874)
-- [Integrated Gradients: Axiomatic Attribution for Deep Networks](https://arxiv.org/abs/1703.01365) | [GitHub](https://github.com/ankurtaly/Integrated-Gradients)
-- [Know Data Science](https://www.linkedin.com/feed/update/urn:li:activity:6516283940658089984)
-- [Understand How to answer Why](https://www.linkedin.com/feed/update/urn:li:activity:6519055798948204544)
-- [Learning with Explanations](https://www.youtube.com/watch?v=m1GUhPgstvk) by [Tim Rocktäschel](https://rockt.github.io/)
-- Towards Explainable AI: [Slides](../presentations/data/03-meetup-uk-2019/Towards-Explainable-AI.pdf) | [Video](https://www.youtube.com/watch?v=0yFjSs-azM4) | [Book: A Concise Introduction to Machine Learning](https://www.amazon.co.uk/Concise-Introduction-Machine-Learning/dp/0815384106/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1566160069&sr=8-2) by [Anita Faul](https://www.linkedin.com/in/anita-faul-123750104/)
-- [Machine Learning Project End to End with Python Code (data science focussed)](https://www.youtube.com/watch?v=ekV9QO5KHUY&list=PLcQCwsZDEzFkP9WMe6xvLrd_ZNGqoXOQY&fbclid=IwAR1z7XBl762FLyo-gVvdBDU1iCVqz89K1yfmJS1cbC4rZyEfF-jO30ZsYeY)
-    - Python Project (Classification) : 
-      - Part A: https://www.youtube.com/watch?v=p0snNMCbvN4&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=8
-      - Part B: https://www.youtube.com/watch?v=j4IgXflsZtg&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=9
-      - Part C: https://www.youtube.com/watch?v=kHZmFVDm0QQ&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=10
+See [Post model-creation analysis, ML interpretation/explainability](./model-analysis-interpretation-explainability.md)
 
 ## Statistics
 

diff --git a/data/model-analysis-interpretation-explainability.md b/data/model-analysis-interpretation-explainability.md
@@ -0,0 +1,41 @@
+# Post model-creation analysis, ML interpretation/explainability
+
+## Libraries & packages
+
+- [Yellowbrick](https://www.scikit-yb.org/en/latest/#yellowbrick-machine-learning-visualization) - is a suite of visual diagnostic tools called “Visualizers” that extend the Scikit-Learn API to allow human steering of the model selection process
+- [Shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model
+- [LIME](https://github.com/marcotcr/lime)
+- [4 Python Libraries For Getting Better Model Interpretability](https://www.analyticsindiamag.com/4-python-libraries-for-getting-better-model-interpretability/)
+- [Integrated Gradients: Axiomatic Attribution for Deep Networks](https://github.com/ankurtaly/Integrated-Gradients) | [Paper](https://arxiv.org/abs/1703.01365)
+- [Resources on GitHub on interpretability](https://github.com/topics/interpretability)
+- [Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) - A Curated, but Probably Biased and Incomplete, List of Awesome Machine Learning Interpretability Resources
+
+## Articles, blog posts, papers, notebooks, books, presentations
+
+- [DataRobot: Model Interpretability - What is Model Interpretability in Machine Learning?](https://www.datarobot.com/wiki/interpretability/)
+- [Model Interpretability with SHAP](http://www.f1-predictor.com/model-interpretability-with-shap/)
+- [Interpreting bag of words models with SHAP](https://sararobinson.dev/2019/04/23/interpret-bag-of-words-models-shap.html)
+- [Explain any machine learning model prediction - using SHAP](https://towardsdatascience.com/how-to-explain-any-machine-learning-model-prediction-30654b0c1c8)
+- [Explain ML Models notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/explain-model)
+- [How to explain the prediction of a ML model](https://lilianweng.github.io/lil-log/2017/08/01/how-to-explain-the-prediction-of-a-machine-learning-model.html)
+- [Explaining complex machine learning models with LIME](https://datascienceplus.com/explaining-complex-machine-learning-models-with-lime/)
+- Hermeneutic Investigations: ML Interpreation - why?: [Video](https://www.youtube.com/watch?v=pmdYlahqA_g) | [Slides](https://github.com/daplantagenet/iml.github.io/blob/master/Hermeneutic%20Investigations.pdf) by [Dean Allsopp](http://github.com/daplantagenet)
+- [Explaining Explanations: An Overview ofInterpretability of Machine Learning](https://arxiv.org/pdf/1806.00069.pdf)
+- [Explaining Black-Box Machine Learning Models](https://shirinsplayground.netlify.com/2018/07/explaining_ml_models_code_caret_iml/)
+- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
+- [R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari: Model interpretability](https://www.oreilly.com/library/view/r-machine-learning/9781789807943/dcd398be-3488-423c-942c-69d1eac253f5.xhtml)
+- [Hands-on Machine Learning with R: Interpretable Machine Learning](https://bradleyboehmke.github.io/HOML/iml.html)
+- Tree SHAP
+  - [Consistent Individualized Feature Attribution for Tree Ensembles ](https://arxiv.org/abs/1802.03888)
+  - [Consistent feature attribution for tree ensembles](https://arxiv.org/abs/1706.06060)
+- [Exact SHAP: A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874)
+- [Integrated Gradients: Axiomatic Attribution for Deep Networks](https://arxiv.org/abs/1703.01365) | [GitHub](https://github.com/ankurtaly/Integrated-Gradients)
+- [Know Data Science](https://www.linkedin.com/feed/update/urn:li:activity:6516283940658089984)
+- [Understand How to answer Why](https://www.linkedin.com/feed/update/urn:li:activity:6519055798948204544)
+- [Learning with Explanations](https://www.youtube.com/watch?v=m1GUhPgstvk) by [Tim Rocktäschel](https://rockt.github.io/)
+- Towards Explainable AI: [Slides](../presentations/data/03-meetup-uk-2019/Towards-Explainable-AI.pdf) | [Video](https://www.youtube.com/watch?v=0yFjSs-azM4) | [Book: A Concise Introduction to Machine Learning](https://www.amazon.co.uk/Concise-Introduction-Machine-Learning/dp/0815384106/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1566160069&sr=8-2) by [Anita Faul](https://www.linkedin.com/in/anita-faul-123750104/)
+- [Machine Learning Project End to End with Python Code (data science focussed)](https://www.youtube.com/watch?v=ekV9QO5KHUY&list=PLcQCwsZDEzFkP9WMe6xvLrd_ZNGqoXOQY&fbclid=IwAR1z7XBl762FLyo-gVvdBDU1iCVqz89K1yfmJS1cbC4rZyEfF-jO30ZsYeY)
+    - Python Project (Classification) : 
+      - Part A: https://www.youtube.com/watch?v=p0snNMCbvN4&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=8
+      - Part B: https://www.youtube.com/watch?v=j4IgXflsZtg&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=9
+      - Part C: https://www.youtube.com/watch?v=kHZmFVDm0QQ&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=10