Note on model interpretation

Added note on model interpretation using Skater

README.md

@@ -17,7 +17,7 @@ This repository provides a server side extension (SSE) for Qlik Sense built usin
 
 The current implementation includes:
 
-- **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition model interpretations are providing using [Skater](https://datascienceinc.github.io/Skater/overview.html).
+- **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition, models can be interpreted using [Skater](https://datascienceinc.github.io/Skater/overview.html).
 - **Unupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **Clustering** : Implemented using [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html), a high performance algorithm that is great for exploratory data analysis. 
 - **Time series forecasting** : Implemented using [Facebook Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a modern library for easily generating good quality forecasts.

docs/README.md

@@ -17,7 +17,7 @@ This repository provides a server side extension (SSE) for Qlik Sense built usin
 
 The current implementation includes:
 
-- **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition model interpretations are providing using [Skater](https://datascienceinc.github.io/Skater/overview.html).
+- **Supervised Machine Learning** : Implemented using [scikit-learn](http://scikit-learn.org/stable/index.html), the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition, models can be interpreted using [Skater](https://datascienceinc.github.io/Skater/overview.html).
 - **Unupervised Machine Learning** : Also implemented using [scikit-learn](http://scikit-learn.org/stable/index.html). This provides capabilities for dimensionality reduction and clustering.
 - **Clustering** : Implemented using [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html), a high performance algorithm that is great for exploratory data analysis.  
 - **Time series forecasting** : Implemented using [Facebook Prophet](https://research.fb.com/prophet-forecasting-at-scale/), a modern library for easily generating good quality forecasts.