Airbnb Dashboard

Overview

This project presents an interactive Tableau dashboard analyzing Airbnb data in New York City. The goal was to uncover insights into neighborhood trends, average pricing, and availability throughout the city.

Tools Used

I used a mix of tools to clean, visualize, and present insights from the NYC Airbnb dataset:

• Tableau: Built the interactive dashboard for exploring NYC Airbnb trends
• Excel: Initial data profiling, quick filtering, and validation
• Python (Jupyter Notebook): Used for advanced data cleaning and transformation
• Pandas & NumPy: Processed missing values and aggregated listing data
• Folium: Created an interactive choropleth map to visualize listing density by neighborhood

Problem Statement

How can hosts and analysts better understand pricing, availability, and regional trends in the NYC Airbnb market using visual analytics?

Process

• Explored raw Airbnb dataset for structure, variables, and data issues
• Cleaned data in Jupyter Notebook: removed duplicates, filled nulls, normalized formats
• Validated and organized data in Excel for quick review and compatibility
• Imported cleaned data into Tableau and created visual layers
• Designed user-friendly dashboards with filters and sliders
• Created Python-based choropleth maps to visualize spatial density

Outcome

The final Tableau dashboard allowed for clear exploration of high‑demand neighborhoods, pricing patterns, and gaps in availability. It provided actionable insights for hosts and stakeholders in the short‑term rental market.

🔗 View the Live Tableau Dashboard →

💻 View Case Study on GitHub →

What I Learned

This project helped sharpen my skills in data cleaning and dashboard design. I learned how to turn raw data into a compelling and useful visual story, and how to make dashboards intuitive for users unfamiliar with the dataset.

Interactive Choropleth Map

The map below was created using Python and Folium to visualise Airbnb listing density and pricing trends by neighborhood across NYC.