GSK-1507 (#1361)

* Added customer churn prediction notebook to the docs.

* Added newspaper classification notebook to the docs.

* Added more comments.

* Added airline tweets sentiment analysis notebook to the docs.

* Added fake/real news classification notebook to the docs.

* Added fake/real news classification notebook to the docs.

* Added M5 sales prediction notebooks to the docs.

* Added movie review sentiment notebooks to the docs.

* Finalized integration of the movie_review_sentiment_classification_pytorch_sklearn notebook to the docs.

---------

Co-authored-by: Rabah Abdul Khalek <rabah.khalek@gmail.com>

Author: AbSsEnT

python-client/docs/reference/notebooks/airline_tweets_sentiment_analysis.ipynb

@@ -43,4 +43,16 @@
 :::{card} <h3><center>💸 Insurance charges prediction</center></h3>
 :link: ../../../reference/notebooks/insurance_prediction_lgbm.ipynb
 :::
-:::::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 👍 Customer churn prediction</center></h3>
+:link: ../../../reference/notebooks/churn_prediction_lgbm.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 🏷️ Sales prediction</center></h3>
+:link: ../../../reference/notebooks/m5_sales_prediction_lgbm.ipynb
+:::
+:::::

python-client/docs/reference/notebooks/churn_prediction_lgbm.ipynb

@@ -33,3 +33,26 @@
 :::
 :::::
 
+:::::{grid-item}
+:::{card} <h3><center> 🗞️ Newspaper classification</center></h3>
+:link: ../../../reference/notebooks/newspaper_classification_pytorch.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> ✈️ Airline tweets sentiment analysis</center></h3>
+:link: ../../../reference/notebooks/twitter_sentiment_analysis_huggingface.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 🤥 Fake/Real news classification</center></h3>
+:link: ../../../reference/notebooks/fake_real_news_classification.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 🍿 Movie Review Sentiment Classification</center></h3>
+:link: ../../../reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb
+:::
+:::::

python-client/docs/reference/notebooks/fake_real_news_classification.ipynb

@@ -15,3 +15,14 @@
 :::
 :::::
 
+:::::{grid-item}
+:::{card} <h3><center> ✈️ Airline tweets sentiment analysis</center></h3>
+:link: ../../../reference/notebooks/twitter_sentiment_analysis_huggingface.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 🍿 Movie Review Sentiment Classification</center></h3>
+:link: ../../../reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb
+:::
+:::::

python-client/docs/reference/notebooks/m5_sales_prediction_lgbm.ipynb

@@ -14,3 +14,15 @@
 :link: ../../../reference/notebooks/insurance_prediction_lgbm.ipynb
 :::
 :::::
+
+:::::{grid-item}
+:::{card} <h3><center> 👍 Customer churn prediction</center></h3>
+:link: ../../../reference/notebooks/churn_prediction_lgbm.ipynb
+:::
+:::::
+
+:::::{grid-item}
+:::{card} <h3><center> 🏷️ Sales prediction</center></h3>
+:link: ../../../reference/notebooks/m5_sales_prediction_lgbm.ipynb
+:::
+:::::