Airbnb Booking & Review Trend Analysis

Project Description:

Technologies: Oracle SQL, Tableau, Python, ETL, Star Schema

This project explored how Airbnb booking and review data can be structured and analyzed for meaningful insights. The raw InsideAirbnb dataset included pricing, availability, and review details for over 100,000 listings. To make this usable, I first conducted exploratory data analysis in Python to understand missing values, distribution of prices, and seasonal variations.

From there, I designed a dimensional data warehouse in Oracle SQL using a star schema. This allowed me to organize listings, hosts, reviews, and bookings into a structure optimized for slicing by city, price, time period, and other dimensions. Advanced SQL functions such as LAG, NTILE, and PERCENT_RANK were applied to identify seasonality patterns, price outliers, and ranking of popular neighborhoods.

Finally, I created a set of interactive Tableau dashboards that let users explore booking trends across cities, identify undervalued neighborhoods, and analyze guest review sentiment. The project demonstrates how raw datasets can be transformed into analytics-ready pipelines and dashboards for tourism and real estate analysis.

Skills Showcase:

• Data warehousing using Oracle SQL and star schema design

• Advanced SQL (LAG, NTILE, PERCENT_RANK) for ranking and seasonality

• ETL pipeline development for InsideAirbnb dataset

• Interactive Tableau dashboarding with drill-down filters

• Exploratory Data Analysis (EDA) in Python

Key Insights:

• Entire homes command the highest prices, while private rooms deliver better value with strong guest engagement

• Capitol Hill and Downtown are the most expensive and reviewed neighborhoods

• Availability peaks in summer months, aligning with travel seasonality

• Listings with mid-range pricing and high availability tend to receive the most reviews

This project demonstrates a full-stack analytics workflow — from ETL and data modeling to SQL-driven insight generation and visual storytelling — delivering a strategic decision-support system that could scale for real-world Airbnb hosts, analysts, or data product teams.