🏘️ AIRBNB BOOKING DATA ANALYSIS USING SQL

This project performs an end-to-end exploratory data analysis (EDA) on Airbnb listings using SQL. The goal is to uncover insights into pricing, availability, popular neighborhoods, room types, and host activity to understand the short-term rental market dynamics.

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📊 Project Objectives:

• Analyze Airbnb listings to extract meaningful insights
• Understand pricing trends across room types and locations
• Identify top-performing hosts and high-demand areas
• Practice writing efficient SQL queries for real-world data

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🧾 Dataset Overview:

• Source: Kaggle – NYC Airbnb Open Data

• Size: ~49,000 listings (sample included: airbnb_cleaned.csv)
• Key Features:
  • id, name, host_id, host_name
  • neighbourhood, room_type, price
  • minimum_nights, number_of_reviews, availability_365

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🛠️ Tools & Technologies:

• SQL (SQLite for simplicity, MySQL/PostgreSQL adaptable)
• Python: pandas, matplotlib, seaborn, sqlite3
• Jupyter Notebook for executing queries and plotting results

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📈 Key Analysis Performed:

• 📍 Average price by room type
• 🏘️ Top 3 neighborhoods with the most listings
• ⭐ Listings with the highest number of reviews
• 📅 Average availability per room type

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🚀 How to Run the Project:

Option 1: SQL-Only (Manual Setup)

1. Open your SQL client (SQLite Browser, MySQL, etc.)
2. Run the create_tables.sql and insert_data.sql scripts
3. Execute queries from analysis_queries.sql

Option 2: Python + SQLite (Jupyter)

1. Install requirements:

   pip install -r requirements.txt
   

2. Launch Jupyter and open airbnb_sql_queries.ipynb

3. Run the notebook to perform analysis and view visualizations

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