diff --git a/docs/_docs/quick_start.md b/docs/_docs/quick_start.md
index ec22eb5..4903c0f 100644
--- a/docs/_docs/quick_start.md
+++ b/docs/_docs/quick_start.md
@@ -15,7 +15,7 @@ subsections:
 
 
 
-Prophet follows the `sklearn` model API.  We create an instance of the `Prophet` class and then call its `fit` and `predict` methods.  
+Prophet follows the `sklearn` model API.  We create an instance of the `Prophet` class and then call its `fit` and `predict` methods.
 
 
 The input to Prophet is always a dataframe with two columns: `ds` and `y`.  The `ds` (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be numeric, and represents the measurement we wish to forecast.
@@ -104,7 +104,7 @@ We fit the model by instantiating a new `Prophet` object.  Any settings to the f
 m = Prophet()
 m.fit(df)
 ```
-Predictions are then made on a dataframe with a column `ds` containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method `Prophet.make_future_dataframe`. By default it will also include the dates from the history, so we will see the model fit as well. 
+Predictions are then made on a dataframe with a column `ds` containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method `Prophet.make_future_dataframe`. By default it will also include the dates from the history, so we will see the model fit as well.
 
 
 ```python
@@ -364,19 +364,6 @@ var x = new MutationObserver(function (mutations, observer) {{
             observer.disconnect();
         }}
 }});
-
-// Listen for the removal of the full notebook cells
-var notebookContainer = gd.closest('#notebook-container');
-if (notebookContainer) {{
-    x.observe(notebookContainer, {childList: true});
-}}
-
-// Listen for the clearing of the current output cell
-var outputEl = gd.closest('.output');
-if (outputEl) {{
-    x.observe(outputEl, {childList: true});
-}}
-
                         })                };                });            </script>        </div>
 
 
@@ -396,19 +383,6 @@ var x = new MutationObserver(function (mutations, observer) {{
             observer.disconnect();
         }}
 }});
-
-// Listen for the removal of the full notebook cells
-var notebookContainer = gd.closest('#notebook-container');
-if (notebookContainer) {{
-    x.observe(notebookContainer, {childList: true});
-}}
-
-// Listen for the clearing of the current output cell
-var outputEl = gd.closest('.output');
-if (outputEl) {{
-    x.observe(outputEl, {childList: true});
-}}
-
                         })                };                });            </script>        </div>
 
 
@@ -429,9 +403,9 @@ In R, we use the normal model fitting API.  We provide a `prophet` function that
 library(prophet)
 ```
     R[write to console]: Loading required package: Rcpp
-    
+
     R[write to console]: Loading required package: rlang
-    
+
 
 
 First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).
@@ -509,4 +483,3 @@ An interactive plot of the forecast using Dygraphs can be made with the command
 
 
 More details about the options available for each method are available in the docstrings, for example, via `?prophet` or `?fit.prophet`. This documentation is also available in the [reference manual](https://cran.r-project.org/web/packages/prophet/prophet.pdf) on CRAN.
-