diff --git a/Applications/EDA-examples/Titanic-Dataset.qmd b/Applications/EDA-examples/Titanic-Dataset.qmd
index e913b192..c06ace57 100644
--- a/Applications/EDA-examples/Titanic-Dataset.qmd
+++ b/Applications/EDA-examples/Titanic-Dataset.qmd
@@ -22,6 +22,14 @@ The Titanic dataset is a well-known dataset that contains information about the
 
 The problem we are exploring is binary classification: predicting whether a passenger survived based on their features.The goal of this EDA is to uncover insights that can guide our modeling decisions, such as identifying important features, handling missing data, and addressing bias in the dataset (e.g., imbalances in survival rate by gender or class).
 
+## Set up Quarto environment
+```{bash}
+#| id: install-jupyter
+# This command ensures that the necessary Jupyter and nbformat packages are installed in the Quarto environment.
+# Without these packages, the Python code cells won't execute properly.
+pip install jupyter nbformat
+```
+
 ## Step 0: Looking Up Each Feature
 Before diving into the analysis, it's important to understand what each feature in the dataset represents. This ensures we're interpreting the data correctly and allows us to make informed decisions during the analysis.