This repository presents an analysis of Airbnb's revenue decline and strategies to capitalize on the lifting of travel restrictions. The analysis begins with data cleaning and exploratory data analysis (EDA) conducted in Python. The cleaned dataset was then imported into Tableau for visualizations and addressing the following objectives:
Identify the optimal types of hosts to acquire and determine their preferred locations for recruitment.
Categorize customers based on their preferences to gain insights such as:
- Targeted neighborhoods for marketing efforts.
- Preferred pricing ranges for different customer segments.
- Various property types that align with customer preferences.
- Recommendations for property adjustments to enhance customer satisfaction.
Visualize the current popular localities and properties in New York to leverage their success.
Explore visualizations and insights to develop strategies for increasing visibility and traction for less popular properties.
This repository provides the Python code used for data cleaning and EDA, as well as Tableau visualizations that offer a comprehensive understanding of the revenue analysis at Airbnb. You can refer to the code and visualizations to gain insights and implement actionable strategies for revenue growth.