Hospitality Marketplace Dataset EDA

Questions to Answer

What are guests searching for in the city?
Which inquiries do hosts tend to accept?
What gaps exist between guest demand and host supply?
Any other information that deepens the understanding of the data.

Datasets

There are two datasets from Airbnb:

searches.tsv: Contains a row for each set of searches that a user does for a city.
contacts.tsv: Contains a row for every time that an assigned visitor makes an inquiry for a stay in a listing in the city.

Searches Dataset

The searches dataset contains the following columns:

ds: Date of the search.
id_user: Alphanumeric user_id.
ds_checkin: Date stamp of the check-in date of the search.
ds_checkout: Date stamp of the check-out date of the search.
n_searches: Number of searches in the search set.
n_nights: The number of nights the search was for.
n_guests_min: The minimum number of guests selected in a search set.
n_guests_max: The maximum number of guests selected in a search set.
origin_country: The country the search was from.
filter_price_min: The value of the lower bound of the price filter if the user used it.
filter_price_max: The value of the upper bound of the price filter if the user used it.
filter_room_types: The room types that the user filtered by if the user used the room_types filter.
filter_neighborhoods: The neighborhoods types that the user filtered by if the user used the neighborhoods filter.

Contacts Dataset

The contacts dataset contains the following columns:

id_guest: Alphanumeric user_id of the guest making the inquiry.
id_host: Alphanumeric user_id of the host of the listing to which the inquiry is made.
id_listing: Alphanumeric identifier for the listing to which the inquiry is made.
ts_contact_at: UTC timestamp of the moment the inquiry is made.
ts_reply_at: UTC timestamp of the moment the host replies to the inquiry, if so.
ts_accepted_at: UTC timestamp of the moment the host accepts the inquiry, if so.
ts_booking_at: UTC timestamp of the moment the booking is made, if so.
ds_checkin: Date stamp of the check-in date of the inquiry.
ds_checkout: Date stamp of the check-out date of the inquiry.
n_guests: The number of guests the inquiry is for.
n_messages: The total number of messages that were sent around this inquiry.

Dependencies

-Pandas 2.0.3
-Numpy 1.24.3
-Matplotlib 3.7.1
-Seaborn 0.12.2

How to Use

1.Ensure you have the required dependencies installed.
2.Download the searches.tsv and contacts.tsv datasets from Airbnb.
3.Replace the file paths in the code with the paths to your downloaded datasets.
4.Run the provided Jupyter Notebook cells to perform exploratory data analysis on the datasets.

Feel free to explore and analyze the datasets to answer the key questions outlined above.