JHU Storytelling Dashboard Visualizations with Tableau

This link is the datasets(code) we decided to adopt after discussion (main research content and direction)

https://www.kaggle.com/datasets/teejmahal20/airlin…

Our questions can be classified into seven groups:

1. Age

2. Gender

3. Class

4. Loyalty

5. Type of Travel(This is the part I need to complete)

I need:

– Presentation slides

(and I need a corresponding speech script,

my file: Project Description, roughly describe the content of this

part, not too much, about one page)

– Tableau files

6. Flight Distance & Delay

7. Service Scores

Project Description:
Storytelling Dashboard Visualizations with Tableau
In this project, students will have the opportunity to identify a problem of interest and apply
their skills in data visualization to develop a series of research questions. They will design
and build storytelling dashboard visualizations to address these research questions and
present their findings in a captivating and informative manner, utilizing the techniques
learned in class.
Submission Details:
• Presentation slides
• Tableau files
• One or more published Tableau Dashboard (or views or stories) URL links
• YouTube link to the presentation recording
Content Guidelines for the Presentation:
The presentation should cover (but not be limited to) the following aspects:
• Introduction: Introduction of the team and the project.
• Dataset Description: Provide a description of the datasets used in the project. If possible
and publicly available, include a few rows of the datasets to give an overview.
• Rationale: Explain why the dataset was identified and its relevance to the problem at
hand.
• Research Questions: Present a list of research questions that guided the project.
• Methodology: Describe the methodology used for data cleaning and visualization using
Tableau.
• Storytelling through Visualization: Showcase the key dashboard(s), views, and stories to
support your story.
• Conclusions and Recommendations: Summarize the findings and present any conclusions
or recommendations derived from the analysis.
• References and Sources: Include a list of references and sources used in the project.
Some detailed group discussion content information
⚫
This link is the datasets(code) we decided to adopt after discussion (main research
content and direction)
https://www.kaggle.com/datasets/teejmahal20/airline-passengersatisfaction/discussion/252577#1453239
Our questions can be classified into seven groups:
1. Age
2. Gender
3. Class
4. Loyalty
5. Type of Travel(This is the part I need to complete)
I need:
– Presentation slides
(and I need a corresponding speech script,
my file: Project Description, roughly describe the content of this
part, not too much, about one page)
– Tableau files
6. Flight Distance & Delay
7. Service Scores
⚫ EDA of one of the team members:
https://colab.research.google.com/drive/1x8fGroLmokn9PLuo5T_webQRkK5Lw0G?usp=sharing
please ignore regressors and onwards, just look at the EDA
⚫ Another team member provided some EDA with tableau, which I will attach in a separate
file
Its file name is this:
And another team member’s twbx file, to make sense of it
Its file name is this:
⚫
Suggestions
What are the factors that most influence customer satisfaction?
a. Which factors have the highest correlation with customer satisfaction?
b. Are there specific services that have a greater impact on satisfaction for business travelers
compared to personal travelers?
In Tableau:
Create a correlation matrix heat map that displays how each variable (Inflight wifi service,
Gate location, Online boarding, etc.) correlates with customer satisfaction.
Create a stacked bar chart to show the difference in factor importance between business
and personal travelers.
How does age and customer type affect customer satisfaction?
a. Are loyal customers more likely to be satisfied compared to disloyal customers across
different age groups?
Create a side-by-side bar chart that compares satisfaction levels between loyal and disloyal
customers within different age groups.
How does flight delay affect customer satisfaction?
a. Does departure delay affect customer satisfaction more than arrival delay?
b. Is the effect of delays on satisfaction different between business and personal travelers?
Create a scatter plot that shows the relationship between departure delay and satisfaction
levels. Add a trendline.
Repeat the above step for arrival delay.
Use filters to create multiple views of this relationship for business and personal travelers.
How does in-flight experience contribute to customer satisfaction?
a. How do passengers rate different in-flight services such as seat comfort, food and drink,
entertainment, and legroom?
b. Are ratings for in-flight services consistent across different travel classes (Business, Eco,
Eco Plus)?
create a radar chart to visualize the ratings of different in-flight services.
Use a color-coded line chart to compare ratings of in-flight services across different travel
classes.
How do online services affect customer satisfaction?
a. How does the ease of online booking relate to customer satisfaction?
b. Is online boarding satisfaction related to overall satisfaction?
Create a scatter plot that shows the relationship between the ease of online booking and
customer satisfaction. Add a trendline.
Create another scatter plot that shows the relationship between online boarding and
customer satisfaction. Add a trendline.
How does cleanliness affect customer satisfaction?
a. Is there a correlation between cleanliness and overall satisfaction?
b. Does cleanliness have different levels of importance for different types of travel?
create a scatter plot that shows the relationship between cleanliness ratings and customer
satisfaction. Add a trendline.
Create a box plot to compare cleanliness ratings by type of travel (business vs personal).
⚫
Some tips on how to deliver our presentation:
1. Spend 10s on introducing the team
2. Which project and dataset we are working with
3. 10s on why choose this dataset
4. Questions / Hypothesis that we try to address
5. Explain data cleaning / Python part
6. Have some recommendations