https://github.com/renukadhule/airbnb_listings_and_reviews_python_analysis
Explore Airbnb listings and reviews from over 250,000 properties across 10 major cities. Dive into data on listing details, host information, pricing, and customer reviews for better insights
https://github.com/renukadhule/airbnb_listings_and_reviews_python_analysis
matplotlib-pyplot numpy pandas python seaborn
Last synced: 5 months ago
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Explore Airbnb listings and reviews from over 250,000 properties across 10 major cities. Dive into data on listing details, host information, pricing, and customer reviews for better insights
- Host: GitHub
- URL: https://github.com/renukadhule/airbnb_listings_and_reviews_python_analysis
- Owner: renukadhule
- Created: 2025-01-18T14:31:30.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-04T03:08:20.000Z (10 months ago)
- Last Synced: 2025-03-20T07:47:16.885Z (8 months ago)
- Topics: matplotlib-pyplot, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 835 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# AirBnB Listings & Reviews
## Project Overview
This project analyzes Airbnb listing and review data to uncover valuable insights into the factors influencing the success of Airbnb listings.
By examining key attributes such as listing features, host behavior, and review patterns, this analysis aims to help hosts optimize their listings and provide potential guests with data-driven recommendations
## Dataset
[get the dataset here](https://www.kaggle.com/datasets/mysarahmadbhat/airbnb-listings-reviews)
## Build With
**Programming Language**: Python
**Libraries**: Numpy, Pandas, Matlpotlib and Seaborn
## Final Insights
- The Number of AirBnBs kept on increasing since the launch and prices kept on increasing too, due intial traction and early adopters
- After the startups is known to everyone and becomes a common utility, AirBnB starts increasing in numbers and prices also kept on decreasing.
- After regulations was announced around 2015 there was under confidence in the business, number of AirBnBs started decreasing and prices started increasing.
- Once the regulations is the new normal, during the year 2019 the number of AirBnBs have increased in number and prices kept increasing due to more supply of them.
## Recommendations for AirBnB
- Regulations in long term rentals can impact the business adversily, there might be customer and host churn due to uncertainty.
- Such regulations might add to AirBnb losses which might be difficult to recover later
- If the customer experiance is going to get impacted due to this, it would lead to to incorrect brand perception
- It is recommend to watch out any such regulations at other places and be prepared for it.
- AirBnB can replicate such regulations at other places.
- They can keep strict rules to onboard and release the hosts.
- They can limit the number of AirBnBs in a locality to ensure the public has enough rental options and the government does not step in
## Conclusion
This project provides valuable insights into Airbnb listings and guest experiences. It can help hosts improve their listings based on data-driven findings, and guests can use these insights to make better decisions when booking properties