Airbnb Open Data – Exploratory Data Analysis

A simple data analysis mini-project on the Airbnb Open Data (NYC listings). This was made as part of my data analysis coursework using Python, Pandas, Matplotlib and Seaborn.

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📌 Project Goal

To clean the Airbnb dataset and explore it visually to answer questions like:

• Which neighbourhood group has the most listings?
• What is the average price in each area?
• Which room types are most common?
• How are reviews and prices related?

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📁 Project Structure

Event_Data_Analysis_Project/
│
├── Airbnb_Open_Data.xlsx      # Original dataset
├── airbnb_eda.py              # Main analysis script
├── airbnb_cleaned.csv         # Cleaned dataset (created after running)
├── requirements.txt           # Python libraries needed
├── index.html                 # Project website
├── style.css                  # Website styling
├── images/                    # Charts created by the script
└── README.md

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⚙️ How to Run

1. Clone this repository

   git clone https://github.com/ParvathyM155/Event_Data_Analysis_Project.git
   cd Event_Data_Analysis_Project

2. Install the required libraries

   pip install -r requirements.txt

3. Run the analysis script

   python airbnb_eda.py

4. Open images/ to see the generated charts, or open index.html in a browser to view the project website.

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🧹 Data Cleaning Steps

• Renamed columns to lowercase with underscores
• Fixed typos in neighbourhood_group (brookln → Brooklyn, manhatan → Manhattan)
• Dropped the almost-empty license column
• Removed duplicate rows
• Removed rows missing important fields (price, room_type, etc.)
• Filtered out unrealistic values (price ≤ 0, minimum nights > 365, etc.)

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📊 Charts Generated

File	What it shows
listings_by_group.png	Number of listings per neighbourhood group
room_type_distribution.png	How common each room type is
avg_price_by_group.png	Average price in each neighbourhood group
price_distribution.png	Overall distribution of prices
price_by_room_type.png	Price comparison across room types
top10_neighbourhoods.png	Top 10 neighbourhoods by number of listings
reviews_vs_price.png	Relationship between reviews and price
heatmap.png	Correlation heatmap of numeric columns

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🌐 Website

A simple HTML/CSS page (index.html) is included to present the project. You can host it for free using GitHub Pages:

1. Push the project to GitHub
2. Go to Settings → Pages
3. Under Source, choose main branch and /root
4. Save — your site will be live at https://<your-username>.github.io/Event_Data_Analysis_Project/

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🛠️ Tools Used

• Python 3
• Pandas
• Matplotlib
• Seaborn
• Jupyter / Google Colab (for early exploration)

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👩‍🎓 Author

Parvathy M

Data Analysis Mini Project