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.

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?

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

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.

• 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.)

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/

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

Parvathy M

Data Analysis Mini Project