Airbnb Bookings Analysis Project

Figure: A visual preview of the Tableau dashboard showcasing key insights and metrics.

Overview

Welcome to the Airbnb Bookings Analysis project repository! This project explores the intricate world of Airbnb bookings through an in-depth Exploratory Data Analysis (EDA). The primary goal is to uncover valuable insights and trends within the dataset, shedding light on factors influencing booking patterns, user behavior, and pricing strategies. The project employs Python for data analysis and visualization, utilizing libraries such as Pandas, Matplotlib, Seaborn, and Tableau.

Project Goals

Understand Hosting Dynamics:

• Identify peak hosting periods.
• Discover popular listing types.
• Determine the average duration of stays.

User Preferences:

• Investigate factors such as location, property type, amenities, and pricing.
• Provide insights into what guests value the most.

Pricing Strategies:

• Explore the relationship between pricing and booking frequency.
• Identify optimal pricing ranges for different property types and locations.

Location Analysis:

• Utilize geospatial data to visualize the distribution of Airbnb listings.
• Identify hotspots and understand regional variations in demand.

How to Use

1. Clone the repository to your local machine.
2. Open the Jupyter Notebook 'DATA EXPLORATION,WRANGLING & VISUALIZATIONS.ipynb' for data analysis.
3. Execute the cells within the Jupyter Notebook to run the data analysis code.
4. Open the Tableau Dashboard hosted on Tableau Public for interactive visualizations.

Dependencies

• Python 3.x
• Pandas
• Matplotlib
• Seaborn
• Tableau (for interactive visualizations)

Contributions

Contributions are welcome! If you have ideas for improvements, enhancements, or new features, feel free to submit a pull request.

Enjoy exploring the fascinating world of Airbnb bookings! 🏠✨