Inspiration
Our inspiration comes from the StrataScratch Challenge in the Atlantis Datathon, where we were tasked with analyzing Airbnb data for Dublin, Ireland. We aimed to uncover insights into the relationship between host offers and guest demand to improve the Airbnb market in Dublin.
What it does
Our project analyzes Airbnb data from Dublin to understand patterns from guest preferences, bookings, and host acceptance rates. This visualizes the data to identify gaps between demand and supply and provide recommendations for increasing bookings in Dublin. This project also brings awareness to trends that affect ones bookings or searches.
How we built it
Using DeepNote, we utilized the data from searches.tsv and contacts.tsv--which were converted into .csv files for the purpose of analysis--and created data visualizations with Python (pandas, matplotlib, seaborn) and SQL to communicate our findings effectively.
Challenges we ran into
One of the main challenges we encountered was dealing with missing or inconsistent data in the Airbnb datasets. We had to handle data cleaning and preprocessing to ensure the accuracy of our analysis, especially when there were NULL values or outliers.
Accomplishments that we're proud of
We're proud of our analysis of the Airbnb data. We were able to create visualizations that effectively communicate patterns that we found in the in the data, which was an accomplishment because this was a first for most of us, so overcoming our learning curve to utilize Python libraries and SQL to create graphs was a big success.
What we learned
Through this project, we gained hands-on experience in data analysis and visualization techniques by learning how to clean and preprocess data from real-world datasets, and discover meaningful insights to improve business decisions. Additionally, that data projects require attention to detail and help from all members apart of the collaboration.
What's next for Atlantis Datathon
For the Atlantis Datathon, the next steps could involve further exploring the Airbnb data to uncover additional insights, such as researching more external factors for spikes in bookings and creating pricing strategies based on our findings to help managers increase the number of bookings in Dublin.
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