🏠 Airbnb Data Analysis

This project performs Exploratory Data Analysis (EDA) on Airbnb listings using data from Inside Airbnb. The goal is to understand pricing patterns, neighborhood trends, and availability of listings.

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📌 Project Summary

• Project Name: Airbnb Data Analysis
• Tools Used: Python, Pandas, Matplotlib, Seaborn, Folium
• Dataset: Inside Airbnb (e.g., listings.csv)
• Focus Areas:
  • Price distribution
  • Neighborhood trends
  • Room type analysis
  • Availability and review insights
  • Interactive map visualization

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📂 Dataset Description

The dataset contains detailed information about Airbnb listings, including:

• Listing ID, Name, Host Info
• Neighborhood, Latitude & Longitude
• Room Type
• Price, Minimum Nights, Number of Reviews
• Availability over the year

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🧪 EDA Highlights

• 📊 Room Type Analysis: Distribution and average price by room type
• 💰 Price Distribution: Focus on listings below $500
• 📍 Neighborhood Insights: Top 10 areas by number of listings
• 🔢 Minimum Nights & Review Distribution: Histograms for clearer trends
• 🔁 Availability vs Price: Scatter plot for listing availability
• 🔥 Correlation Heatmap: Relationship between price, reviews, and availability
• 🌍 Folium Map: Interactive map of 300 sample listings priced below $300