This is a bar graph of top five word frequencies in our feedback submissions.
This is a bar graph of our satisfaction ratings.
Our inspiration was to create an easier customer experience with JetBlue Airlines and to give customers the power to give feedback and make change.
What it does
Allows customers to give feedback in an efficient way. Allows the to contact customer service with out having to search for them. and to follow JetBlue on social media and stay up to date with what's going on
How we built it
We started by using Appy-pie to provide us with a baseline of how we wanted to build our app. Then we created 50 mock feedback submissions to represent real customer feedback. From there we gathered the data and organized it in excel so it was cohesive and easy to read. After putting the data together in excel, We started with python. In python, we created a text blob with the user feedback. We then used _ word_count _ to retrieve five most used words throughout the users feedback. We also looked at the satisfaction ratings from the app and used _ word_counts _ again to find what user were rating their experience as. Then we created a bar graphs to show our data.
Challenges we ran into
We had trouble figuring out how to prove a correlation between the satisfaction rating and its cause.
Accomplishments that we're proud of
Our Teammate made her first app
What we learned
We learned appy-pie. And more about the _ pandas _ package in python
What's next for JetBlue Feedback App
Being able to handle larger data sets and making the app more efficient.