Airbnb Analysis in Paris

Overview

This GitHub repository contains an analysis of Airbnb data in Paris, with a specific focus on understanding the economic and social impact of the increase in listings and multi-listing hosts. The project was completed as part of the course "Data Management & Ethics" in the MScBa Business Analytics & Management program at RSM (Rotterdam School of Management, Erasmus University).

Project Goals

The primary objectives of this project are as follows:

1. Data Analysis: Perform in-depth data analysis on Airbnb listings in Paris using SQL, Python, and data visualization with Matplotlib and seaborn.
2. Economic and Social Impact: Investigate and report on how the proliferation of Airbnb listings and multi-listing hosts affects both the economic and social aspects of Paris.
3. Data Sources: Utilize data provided by Inside Airbnb, which is stored in the "data" directory.

Tools Used

The following tools and technologies were employed in this analysis:

• SQL: To query and manipulate the dataset.
• Python: To conduct data analysis and generate insights.
• Matplotlib, seaborn, pandas, geopandas : To create data visualizations and easily analyse the data.
• Jupyter Notebooks: For a detailed and interactive presentation of the analysis.

Project Structure

The repository is structured as follows:

• data: This directory contains the Airbnb dataset sourced from Inside Airbnb. The data serves as the foundation for our analysis.
• queries: The SQL queries used to extract and transform the data.
• results: The "results" directory contains the output of the SQL queries, which are the transformed datasets.
• report: This directory holds a report in PDF format summarizing the findings of the analysis. It also includes the Entity-Relationship Diagram (ERD) of our database.

How to Use

If you want to explore this project, you can follow these steps:

1. Clone this repository to your local machine.
2. Access the Jupyter notebooks in the root directory for a step-by-step walkthrough of the analysis.
3. Explore the SQL queries and results in the "queries" and "results" directories.
4. Read the detailed report in the "report" directory for a comprehensive overview of our findings.