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Airbnb Seattle data investigation

Introduction

Airbnb,‌ ‌I‌n‌c‌.‌ is a company based in San Francisco that operates an online marketplace and hospitality service. It allows people to lease or rent short-term lodging including holiday cottages, apartments, homestays, hostel beds, or hotel rooms, to make reservations at restaurants etc.

  • How good reviews are achieved?
  • Is it possible to make an accurate predictive model for listing price based on machine learning?

Question 1: How the best price is achieved on Airbnb?

We answer this question by plotting a heatmap of correlations and we focus on the 7th row (price).

Accommodation’s linear relation with Price (at least from 1 to 8 rooms)
  • The listings need to be in the right area and its also important to get good reviews but not as important as to accommodate as many as possible.
  • Having a TV and a parking spot is also advised; Install a washer and air condition

Question 2: How good reviews are achieved?

As mentioned above, the room-related features like the capacity of people would drive the prices. Similarly, having amenities which has a cost attached drive more prices. But one of the factors that might not influence pricing but can push the demand and general likeability of the property is, what others say?

Review score mapped vs. Host Response Rate
  • Respond on every inquiry you get
  • Don’t have limitations on nights to stay
  • Include amenities like parking space, TV and Internet
  • High availability (this one you might disregard if you want to make money :) )

Question 3: Is it possible to make an accurate predictive model for listing price based on machine learning?

I experimented with three different machine learning algorithms for this analysis: AdaBoost, Support vector machines and RandomForest.