Exploratory Data Analysis on Airbnb Booking Data
Exploratory Data Analysis on Airbnb Booking Data
Embedded Files
This is the Capstone project which is a part of my Data Science course at AlmaBetter.
This is the Capstone project which is a part of my Data Science course at AlmaBetter.
Data analysis on thousands of listings provided through Airbnb is a crucial factor for the company. My main objective is to find out the key metrics that influence the listing of properties on the platform.
Data analysis on thousands of listings provided through Airbnb is a crucial factor for the company. My main objective is to find out the key metrics that influence the listing of properties on the platform.
About the Company
Airbnb is an online marketplace that connects people who want to rent out their homes with people who are looking for accommodations in that locale. It currently covers more than 100,000 cities and 220 countries worldwide. For hosts, it's a way to earn money while protecting their property from potential damage.
Goal of this Project
To find out the key metrics that influence the listing of properties on the platform.
Tools Used
Python Libraries (Numpy and Pandas) for Data cleaning and Analysis
Python Libraries (Matplotlib and Seaborn) for Visualization
Github Repo link: https://github.com/Shantanuh10/Airbnb_EDA_Project-
❖ Finding Total count of each room type as per listing.
❖ Finding Total count of each room type as per listing.
Based on the Analysis we can say that:
➔ Entire home/apt has the highest number of listing of 52% among other room types.
➔ Private room has 45.7% of listing among other room types.
➔ Shared Room is the least listed room type at only 2.4% in total.
❖ Room types and their relation with availability in different neighborhood groups.
❖ Room types and their relation with availability in different neighborhood groups.
As we can see every neighborhood group is dominated by the private room type at an average of 55%.
❏ Brooklyn and Manhattan have the least availability of rooms overall as low as 0 days.
❏ Staten Island and Bronx has the highest availability rate overall at around 300 days.
❏ Form this analysis we can say that people stay for longer duration of time in Private rooms in Brooklyn and Manhattan.
❖ Finding top 25 most common words used in listing names.
❖ Finding top 25 most common words used in listing names.
❏ As we can see most of the listing names include words related to property type such as ‘bedroom’, ‘cozy’, ‘private’, ‘apartment’ and ‘spacious’.
❏ It is interesting to see that words related to proximity or connection to public places such as ‘park’, ‘near’, ‘village’ and ‘heart’ rank lower in chart.
Page updated
Google Sites
Report abuse