https://github.com/parthds02/airbnb-listings-eda-project-new-york-2024

This project analyzes Airbnb listing data to uncover trends in pricing, reviews, and availability. Using Python, it explores correlations between variables such as price, room type, and location. Visualizations and statistical insights help hosts optimize listings for better performance in competitive markets like New York.
https://github.com/parthds02/airbnb-listings-eda-project-new-york-2024

airbnb data-science dataanalysis-projects newyork panadas python vscode

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This project analyzes Airbnb listing data to uncover trends in pricing, reviews, and availability. Using Python, it explores correlations between variables such as price, room type, and location. Visualizations and statistical insights help hosts optimize listings for better performance in competitive markets like New York.

• Host: GitHub
• URL: https://github.com/parthds02/airbnb-listings-eda-project-new-york-2024
• Owner: ParthDS02
• License: mit
• Created: 2024-10-17T09:16:56.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-17T09:18:54.000Z (about 1 year ago)
• Last Synced: 2025-01-10T23:42:30.872Z (10 months ago)
• Topics: airbnb, data-science, dataanalysis-projects, newyork, panadas, python, vscode
• Language: Jupyter Notebook
• Homepage:
• Size: 3.28 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
### Airbnb Listings EDA Project: New York 2024  

---

## Project Overview

This project performs **Exploratory Data Analysis (EDA)** on New York Airbnb data to uncover trends and patterns in rental listings. We use libraries like **Pandas, Numpy, Matplotlib, Seaborn**for cleaning, visualization, and analysis. 

![](https://github.com/najirh/Python-Project-P2-New-York-AirBnb-Listing-2024/blob/main/New-York-City-Brooklyn-Bridge-Panorama-Juergen-Roth-2.jpg)

---

## Objective

The goal of this project is to:

1. Analyze **room types, prices, and availability** across different neighborhoods.

2. Understand **host behavior** and listing patterns.

3. Detect potential **outliers** in prices.

4. Provide recommendations for guests and hosts based on insights.

---

## Dataset

The dataset contains **20,765 entries and 22 features**, including:

- **id**: Unique identifier for each listing  

- **name**: Title of the Airbnb listing  

- **host_name**: Name of the host  

- **neighborhood_group**: Group (borough) where the listing is located  

- **latitude/longitude**: Geolocation of listings  

- **price**: Nightly rental price  

- **room_type**: Type of accommodation (e.g., entire home, private room)  

- **reviews_per_month**: Average monthly reviews for the listing  

- **availability_365**: Number of available days in the year  

---

## Steps and Workflow

### 1. Data Cleaning

- **Handle missing data**: `price`, `neighborhood`, and `beds` columns had null values.

- **Fix data types**: Converted `last_review` to a **datetime** object.

- **Remove outliers**: Listings with prices > $1,000 were capped to avoid skewed visualizations.

### 2. EDA (Exploratory Data Analysis)

1. **Room type distribution**: 

   - Visualized the count of each room type using **bar plots**.

   - Identified **Entire home/apt** as the most common room type.

2. **Neighborhood group insights**:

   - Analyzed **price variations by boroughs**.

   - Manhattan had the **highest average prices**.

3. **Availability trends**:

   - Used **heatmaps** to show correlations among `price`, `availability_365`, `number_of_reviews`, and `beds`.

4. **Price distribution**:

   - Used **histograms** to show the distribution of prices.

   - Majority of the listings were priced between **$50 - $300**.

5. **Host listings**:

   - Analyzed hosts with multiple listings using **boxplots** to identify key contributors.

6. **Review behavior**:

   - Used **pair plots** to show relationships between number of reviews, price, and availability.

### 3. Data Visualization

- **Pairplot**: To see correlations among `price`, `availability`, and `number of reviews`.

- **Heatmap**: Showing correlations among numerical features.

- **Histograms and Boxplots**: To detect outliers in `price`.

- **Bar Charts**: Displaying room types and neighborhood group distributions.

---

## Key Findings and Insights

1. **Price Trends**:  

   - **Manhattan** has the most expensive listings, followed by Brooklyn.  

   - **Entire homes/apartments** cost significantly more than private or shared rooms.  

2. **Room Type Distribution**:  

   - **Entire homes/apartments** are the most common, but **private rooms** offer budget-friendly options.

3. **Outliers in Price**:  

   - Few listings priced at **$10,000+** were detected, indicating the need to filter such extreme values.

4. **Availability Patterns**:  

   - Listings with **high availability** tend to have lower prices and more reviews, likely due to better guest experience.

5. **Host Behavior**:  

   - Some hosts manage **multiple listings**, indicating a trend toward professional hosting.

## Recommendations

- **For Guests**: 

   - Look for listings with high availability and good reviews for a better experience.

   - **Private rooms** in Brooklyn offer affordable stays compared to Manhattan.

- **For Hosts**:  

   - Improve **availability** and **review response rates** to attract more bookings.

   - Manage pricing effectively to compete within the borough's market.

   

---

## Conclusion

This project offers valuable insights into the New York Airbnb market, helping both guests and hosts make informed decisions. By using **EDA techniques**, we identified key trends and developed actionable recommendations. Future improvements can involve advanced analytics and predictive modeling to further enhance the findings.