Seattle Airbnb Exploratory Data Analysis
By: Tara Devi
Tags: Exploratory Data Analysis, Data Science, Machine Learning

In this Blog we can look at Seattle airbnb data listings and using CRISP-DM process to analyze the data.

CRISP-DM (Cross-Industry Standard Process for Data Mining) consists of:

1) Business Understanding

2) Data Understanding

3) Data Preparation/ Wrangling

4) Data Modeling

5) Data Evaluation

6) Deployment

Detailed analysis with all required codes, data sets is posted in my github repository.

The Dataset describes the types of accommodations along with the amenities in Seattle, WA with an average review scores.

This project provides you the answers for the below questions of interest.

What are the most common amenities?

What are the features that attracts the guests most for renting a property?

What are the attributes associated with the price of a listing?

· What is the impact on price if the property is close to downtown?

Question- 1: “What are the most common amenities?”

This listing provides various different kinds of amenities. Some of them are common and some are available only in this property.

In the below chart, you can see the most common amenities and least common amenities.

The most common amenities in Seattle listings are:

· Internet

· Kitchen

· Heating

· Smoke Detector

The least common amenities in Seattle listings are:

· Indoor Fireplace

· Hangers

· Elevators in Building

· Other pet(s)

Question- 2: “What are the features that attracts the guests most for renting a property?”

Some Unique features of listing are the types of rooms and the different kinds of amenities available will attract the guests to rent this property.

Types of rooms are shown as per the below chart.

You can also see that in next 30 days nearly half of the bookings for Home/apt are full. Whereas private rooms and shared rooms are less booked, assuming that they are less popular compared to Home/apts.

In the next 30 days period, 80% of “Tent” listings are full. So there is interest in renting tents, but this could be seasonal too. There are only 5 tent listings available so this could be a second reason why Tents seem to be popular.

The most popular property type with high number of listings is “Apartment”. Nearly half of them are full in the next 30 days period.

According to the table above, having a Washer/Dryer increases the booking rate for next 30 days more than 15%. Also having a kitchen and allowing pets are good ideas for increasing the popularity for a listing.

It looks like having a pool or serving breakfast does not help a listing to be preferred, probably because of the increased price with these amenities.

Question- 3: “what are the attributes associated with the price of a listing?”

Using Linear Regress model to estimate will give us the best results on price listing, as the model has an r-squared value around 0.62 which is not very successful.

Here are the top 15 most influential features of the model:

Average boat listing price is more than 282 USD whereas an average listing has a price of 128 USD per night. So property_type_boat has a significant effect on price of listing.

In the above table, positive coefs increase the price whereas negative coefs have a negative effect on price, so they lower the price of the listing.

Question- 4: “What is the impact on price if it is be close to downtown?”

The distance is calculated by Coefficient feature. Each listing’s distance to Downtown. After training the model, the coefficient for this new feature is in the table below:

The ‘distance_from_downtown’ feature has a negative effect on price. As the distance from downtown increases, the price of listing decreases.

Example: For one kilometer additional distance from downtown, the price of listing decrease around 3 USD.

Regards,

Tara Devi

References

Github: https://github.com/Tara889/Airbnb-seattle-Exploratory-data-analysis.git