Airbnb Booking Analysis using EDA

This project performs exploratory data analysis (EDA) on Airbnb booking data to uncover valuable insights, trends, and patterns. The analysis involves cleaning, visualizing, and deriving conclusions from the dataset.

Table of Contents

• Project Overview
• Dataset
• Features
• Technologies Used
• Setup Instructions
• Insights
• Contributing

Project Overview

Airbnb is one of the most popular platforms for booking vacation rentals. Analyzing its booking data can provide insights into customer behavior, pricing trends, and property characteristics. This analysis helps property owners and potential investors make data-driven decisions.

Dataset

The dataset used for this project can be downloaded from the following link:
Download Airbnb Booking Open Dataset

Dataset Features

• Property ID: Unique identifier for each property.
• Host Name: Name of the property host.
• Room Type: Type of room offered (e.g., entire home, private room).
• Location: Location of the property.
• Price: Price per night.
• Availability: Number of available days in a year.

Features

• Data cleaning and preprocessing.
• Visualizations for property types, pricing, and customer preferences.
• Brief explanations are provided for easy understanding.

Technologies Used

• Python
• Pandas, NumPy for data manipulation.
• Matplotlib, Seaborn for data visualization.
• Jupyter Notebook or Google Colab for development.

Setup Instructions

1. Clone the repository.

2. Download the dataset from the link above.

3. Install the required Python libraries.

4. Run the notebook or script to start the analysis.

Insights

1. Pricing Distribution:

• Most Airbnb listings are priced within a moderate range.
• There are a few high-priced outliers, indicating some premium listings with significantly higher prices.

2. Room Type Distribution:

• The majority of listings are either entire homes/apartments or private rooms.
• Shared rooms and hotel rooms constitute a very small portion of the listings.

3. Geographical Distribution:

• Listings are predominantly concentrated in popular areas like Brooklyn and Manhattan.
• Other boroughs such as Queens, Bronx, and Staten Island have fewer listings.

4. Price Comparison by Room Type:

• Entire homes/apartments generally cost more than private rooms.
• Shared rooms tend to have the lowest prices among the room types.

5. Seasonal Trends in Reviews:

• There are observable seasonal trends in the number of reviews.
• Certain months experience higher review activity.

Contributions

Contributions are welcome! Feel free to fork this repository and submit a pull request.