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Analyze the Airbnb dataset describing the listing activity of home-stays in Seattle, WA to bring insights

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Seattle-Airbnb

This dataset describes the airbnb listing activity of home-stays in Seattle, WA.

Project Overview

The analyses is done on the Seattle Airbnb open data from 2019 that has a listing for 3,818 houses. The data set captures information around the housing availability throughout the year calendar, listing details like area, pricing, house details, and reviews.

Data Overview

The data has been taken from Kaggle: https://www.kaggle.com/airbnb/seattle

The data set has 3 main datasets:

  • Calendar: Shows the availability of each listing through out the year along with the listed price per night
  • Listings: Has all the listings along with the host and house details
  • Reviews: List the reviews each hosts has receieved for all their previous hostings

Business questions answered from the analyses:

  • How and why does price of the house vary through out the year?
  • What has been the most busiest time in Seattle?
  • Which key features impact / drives the house price?

Install

This project requires Python 3.6 and the following Python libraries installed:

  • NumPy
  • Pandas
  • matplotlib
  • seaborn
  • sklearn

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select Python 3.x installer.

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Analyze the Airbnb dataset describing the listing activity of home-stays in Seattle, WA to bring insights

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