A deep dive into the Paris AirBnb dataset to get a better understanding on successful hosts on AirBnb. What is needed to gain Superhost status based on various attributes and predict if hosts are on their way to gain that status.
1. Background & Motivation
2. Data Source
2a. About The Data
3. Exploratory Data Analysis
3a. Listings Based on Average Price
3b. Languages Spoken by Renters
3c. Number of Listings Per Month
3d. Number of Listings Per Host
3e. Hosts vs Superhosts
4. Modeling
4a. Models without Verification Dummies
4b. Models WITH Verification Dummies
5. Conclusions
6. Future Plans
7. Dependencies
8. Contact
AirBnb is an online marketplace that connects people who want to rent out their homes with people who are looking for accommodations in that location. There is a wide selection of properties for travelers to rent for a period of time. Hosts don't have to pay to list their properties on AirBnb's marketplace and they can also set their own pricing. Locals in the area can also list their services and guides to entertain guests as well. With over tens of millions of users a year, the number of listings as well as competitiveness of bringing in renters have increased. As of 2019, AirBnb was last evaluated at $31 billion. My project here is to get a better understanding of the ins and outs of being a successful host on Airbnb.
AirBnb Paris Data Downloaded from
http://insideairbnb.com/get-the-data.html
Paris -
-
listings.csv: (65493 rows, 106 columns) 241.3 MB
Attributes: id, name, summary, space, description, host_id, host_since, host_about, host_response_time, host_response_rate, host_is_superhost, host_total_listings_count, host_verifications, host_has_profile_pic, host_identity_verified, latitude, longitude, property_type, accommodates, bathrooms, bedrooms, beds, amenities, price, security_deposit, cleaning_fee, minimum_nights, number_of_reviews, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, cancellation_policy, requires_license, reviews_per_month, require_guest_phone_verification, language. -
reviews.csv: (1310351 rows, 6 columns) 441.3 MB
Attributes:
listing_id, date, reviewer_id, comments, language_code, language. -
calendar.csv: (23905452 rows, 7 columns) 1.02 GB
Attributes:
listing_id, date, available, price, minimum_nights, maximum_nights, week (generated), month (generated), year (generated).
The daily average price for AirBnb listings in Paris are below $300 USD per day. Some daily prices average out at the extreme end, like $2700 USD.
Looking further into the histogram bar of $0-$300, the daily average price falls mainly between $33 to $100.
A huge majority of renters speak English or French. Primarily 66.19% of the renters speak English while 22.52% speak French. Altogether, the remaining language spoken by renters visiting Paris totals only 11.29%.
The furture number of listings in Paris for 2020 fluxuates depending on the month. February 2020 currently had the least amount of available listings. Whereas January, March, and May have the most listings available.
49580 AirBnb Hosts have between 1 to 12 listings. A few amount of hosts have more than 45 properties listed.
Only 12.81% of AirBnb hosts in Paris are verified Superhosts.
Fitted the dataset into a Logistic Regression, Gradient Boosting, Random Forest, and Decision Tree model. This ROC curve shows the true positive rate against the false positive rate.
Modified model to take in training set without Dummies of Host Verifications and saw a slight increase in average accuracy and an increase in average recall.
In conclusion, the models have a good predictive accuracy on whether a host is on it's way to become a superhost based on certain attributes.
- If given more time, would try to tokenize based of language.
- Find a way to translate through all without hitting requests limit with googletrans module.
- Add dataset of previous years
- Create a time series and predictor on pricing
https://pypi.org/project/iso-639/
https://pypi.org/project/langdetect/
https://pypi.org/project/langua/
Feel free to contact me about any questions. I can be reached through these links.
LinkedIn
Email
version 1.0 - EDA on Superhosts on Airbnb, number of listings available, average pricing.