🏙️ Airbnb Storytelling Case Study: A deep dive into Airbnb listings in New York to derive insights and deliver tailored findings for various stakeholders through comprehensive analysis and visualization.

🔗 Resources:

• Python Notebook: Detailed code and analysis.
• PPT I: Technical insights for Data Analysis Managers and Lead Analysts.
• PPT II: Strategic insights for decision-makers and stakeholders.
• Interactive Tableau Dashboard: A dynamic tool for data exploration.

Objective:

• Provide actionable insights into market trends.
• Enhance understanding of property/host acquisitions and customer preferences.
• Offer recommendations to improve Airbnb's marketing and operational strategies.

Presentations:

• Presentation I: Audience: Data Analysis Managers and Lead Analysts. Focus: Technical insights and data-driven observations.
• Presentation II: Audience: Heads of Acquisitions, Operations, and User Experience. Focus: Strategic recommendations and actionable insights.

Key Insights:

• Shared rooms need improvements to boost user satisfaction.
• A distributed host strategy outperforms reliance on top hosts.
• Manhattan and Brooklyn account for over 80% of listings, showing high concentration.
• Lower minimum night requirements enhance customer convenience.

Analysis Overview:

• Data Preprocessing: Cleaned and derived features for better analysis.
• Univariate Analysis: Explored individual attributes like price, room type, and neighborhood.
• Bivariate/Multivariate Analysis: Identified significant patterns and correlations.

Recommendations:

• Inspect shared room listings for quality improvements.
• Focus on collective host performance rather than individual hosts.
• Expand listings in underrepresented neighborhoods.
• Optimize minimum night requirements to enhance customer convenience.

Future Steps:

• Enhanced Data Collection: Include review scores for more comprehensive analysis.
• Clustering Models: Use machine learning to group similar listings for improved targeting.