Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sushant-suresh/airbnb-listings-eda-data-viz-using-python

EDA on NY Airbnb data to uncover trends and patterns in rental listings.
https://github.com/sushant-suresh/airbnb-listings-eda-data-viz-using-python

data-analytics jupyter-notebook python

Last synced: 14 days ago
JSON representation

EDA on NY Airbnb data to uncover trends and patterns in rental listings.

Awesome Lists containing this project

README 

        
# Airbnb Listings EDA & Data Viz Project: New York



![Renting-out-on-Air-BnB-980x655](https://github.com/user-attachments/assets/13abc58f-626a-4c0c-837c-16ecf451f186)



## Project Overview

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



## Objective

The goal of this project is to:

1. Detect and remove potential **outliers** in rental prices.

2. Analyze **price distribution and average price per bed** across different neighborhoods.

3. Analyze the **ratings** distribution for all rental listings.

4. Find relationship between **price** and **rating** of the rental listings.



## Dataset

The dataset contains **20,770 rows and 22 fields**, including:

- `id`: Unique identifier for each listing  

- `name`: Title of the Airbnb listing  

- `host_name`: Name of the host  

- `neighborhood_group`: Area where the listing is located  

- `latitude/longitude`: Geolocation of listings  

- `price`: Per night 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



## Workflow



### 1. Data Cleaning

- **Handle missing data**: Dropped all NULL values from the dataframe.

- **Handle duplicate entries**: Dropped all duplicate entries from the dataframe.

- **Remove outliers**: Listings with **price > $2,000** were capped to avoid skewed visualizations.



### 2. EDA & Data Visualization

1. **Price distribution**: 

   - Visualized the price distribution after removing potential outliers using histogram.

   - Identified **rent < 250$** as the most common rental price range.



2. **Rating Distribution**:

   - Analyzed ratings given by guests.

   - **Ratings > 4** had the max. frequency.



3. **Price Distribution Across Neighbourhood**:

   - Used barplot to show rent across differnet areas in New York.

   - Further categorized it based on room types.



4. **Price/Bed Distribution Across Neighbourhood**:

   - Used barplot to analyze rent per bed across differnet areas in New York.

   - Further categorized it based on room types.



5. **Relationship between Price and Ratings**:

   - Used scatterplot to show the relationship between rental price and ratings given by guests.

   - **Ratings > 4** were common for listings with high rental.



6. **Geographic Distribution of Airbnb Listings**:

   - Used scatterplot to vizualize Airbnb locations using their longitude and latitude values.

   - Found out Hotel Rooms were very less and majority of the room types were either Private or Entire Home.



## Key Findings and Insights

1. Manhattan has the most expensive listings (mostly Hotel Rooms), followed by Brooklyn.

2. Majority of the listings have guest ratings > 4.

3. Few listings priced at $10,000+ were detected and were filtered out.

4. There are high chances of guests getting a good stay under 250$.

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



## Conclusion

This project offers valuable insights into the New York Airbnb listings, 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.