Inspiration With the rising popularity of Airbnb, we aimed to analyze rental trends in New York City to help travelers make data-driven decisions. Our goal was to uncover insights into pricing, demand, and availability across different neighborhoods.
What it does Our interactive Power BI dashboard provides insights into:
Price variations across different neighborhoods. Demand trends, including review counts and availability. Room type distribution and host activity (single vs. multi-listing hosts). Filters to explore data by price range, room type, and neighborhood for a personalized experience.
How we built it Data Cleaning & Processing: Refined the Airbnb dataset in R for consistency. Visualization: Designed interactive charts in Power BI to highlight key trends. Filtering & Insights: Implemented slicers for better user experience, allowing users to filter by budget category, minimum nights, and room type.
Challenges we ran into Handling outliers in pricing and minimum nights required careful filtering. Ensuring data consistency while replacing datasets in Power BI. Balancing interactivity and performance to avoid slow dashboard responses. Accomplishments that we're proud of Successfully created a dynamic and user-friendly dashboard. Identified actionable insights for hosts to optimize pricing and for travelers to find budget-friendly stays. Automated calculations for total cost based on stay duration, making it easier for users to plan stays.
What we learned The importance of data cleaning before visualization. How filters and interactivity enhance decision-making in Power BI. That neighborhood and room type significantly impact Airbnb pricing and demand.
What's next for Airbnb Rentals at New York City Seasonal Trends Analysis: To understand peak vs. off-peak pricing. Predictive Pricing Models: Using historical data to forecast price trends. Mobile-Friendly Reports: To improve accessibility for users on the go
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