AirBnB SQL Project

💡 Inspiration

The inspiration for this project was a curiosity-driven deep dive into AirBnB listing data. The goal was to identify market opportunities by finding hosts who might benefit from a cleaning and maintenance service.

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The CSV files used in this project can be found at the following link: Google Drive.

📌 Project Description

This project focuses on analyzing AirBnB listings in London, UK. After performing exploratory data analysis on the listings, the goal was to identify potential leads for a hypothetical new cleaning and maintenance service.

🔎 Guiding Questions

This project explores key questions to extract meaningful insights from the dataset:

1️⃣ What are the 10 most reviewed listings?
2️⃣ What are the top 5 most expensive neighbourhoods?
3️⃣ Are any hosts fully booked for March 2025, and which hosts have the least number of available dates in March 2025?
4️⃣ Who are the hosts with the least listings?
5️⃣ Identify some hosts which may benefit from a new cleaning service provider.

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🛠️ Tech Stack

• Database: SQLite3
• Language: SQL

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📊 Features

✅ Relational database creation
✅ Table creation and data import
✅ Exploratory data analysis using SQL
✅ Basic report generation for insights

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📥 Installation & Setup

1. Download Data

   • Get the relevant CSV files from this Google Drive link.

2. Set Up SQLite3

   • If SQLite3 is not installed, download it from SQLite Official Website.
   • Verify installation:

     sqlite3 --version

3. Create the Database

   • Open SQLite3 and create a new database:

     sqlite3 airbnb_london.db

4. Import the CSV Data into SQLite

   • Open SQLite shell and run:

     .mode csv
     .import 'calendar.csv' calendar
     .import 'listings.csv' listings
     .import 'listings-2.csv' listings_2
     .import 'neighbourhoods.csv' neighbourhoods
     .import 'reviews.csv' reviews
     .import 'reviews-2.csv' reviews_2

5. Verify Data

   • Open SQLite and explore the data:

     SELECT * FROM listings LIMIT 5;
     

6. Run SQL Queries

   • Open SQLite and explore the data:

     sqlite3 airbnb_london.db

   • Example query:

     SELECT host_id, host_name, AVG(review_scores_cleanliness) AS avg_cleanliness 
     FROM listings 
     GROUP BY host_id 
     ORDER BY avg_cleanliness DESC;

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🚀 Usage

This project serves as a template for:

• Importing web-scraped data into a relational database
• Performing exploratory analysis on structured data
• Extracting actionable insights from raw datasets

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📌 Notes

• No contribution guidelines at the moment.
• No specific license.