Task-5-EDA

🔍 Exploratory Data Analysis (EDA) on Airbnb NYC Dataset

This project explores and analyzes the NYC Airbnb Open Data (2019) to uncover insights into pricing, location trends, room types, and availability using visual and statistical methods.

---

📁 Dataset

• Source: Kaggle – NYC Airbnb Open Data (2019)
• Size: 48,000+ rows, 16 columns
• Format: CSV
• Features: Borough, Neighbourhood, Room Type, Price, Availability, Reviews, etc.

---

🛠️ Tools & Libraries

• Python 3.x
• Pandas
• Matplotlib
• Seaborn
• Google Colab (Notebook Environment)

---

📊 Key Steps

1. Data Loading & Initial Exploration
2. Cleaning:
   • Dropped unused columns
   • Filled missing values
   • Removed outliers (IQR method)
3. Univariate & Bivariate Analysis
4. Visualizations:
   • Countplots
   • Boxplots
   • Heatmaps
   • Geospatial Scatterplots
5. Insights & Summary

---

📌 Notable Insights

• Manhattan has the most listings and highest prices.
• Private rooms dominate in number; entire homes dominate in price.
• Most listings are concentrated in tourist-heavy areas.
• No strong correlation between price and availability/reviews.

---

👤 Author

Sonal Shukla
Aspiring Data Analyst | Python Enthusiast