Skip to content

Latest commit

 

History

History
66 lines (52 loc) · 3.08 KB

README.md

File metadata and controls

66 lines (52 loc) · 3.08 KB
Raw

Market-analysis

Market Analysis in Dublin This data project has been used as a take-home assignment in the recruitment process for the data science positions at Airbnb.

Assignment Overview

This project explores the dynamics of the Airbnb market in Dublin, focusing on guest searches, host responses, and market trends. The objective is to gain valuable insights into demand, supply patterns, and potential market gaps. The project involves thorough data exploration, visualization, and communication of key findings. There are 2 datasets

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

searches dataset contains the following columns:

  • 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 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

Key Findings

Searches Dataset:

  • High null values in 'neighborhood.'
  • Majority prefer 1 or 2 guests.
  • Positive skewness in numeric columns.
  • Searches mainly in October.
  • 14.25% have prices exceeding 600.
  • Room type data may need cleaning.
  • Top origin countries identified.

Contacts Dataset:

  • Positive skewness, especially in guest count.
  • Majority have less than 8 guests.
  • Conversion rate considered.
  • Seasonal check-in patterns revealed.

Merged Datasets Analysis:

  • Comparative analysis.
  • Price impact on acceptance visualized.
  • Price ranges classified.
  • Varying acceptance rates by country.