Potential implications of the expected results

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Reference no: EM131469777

PROBLEM STATEMENT

Airbnb is a trusted and growing community for people who would like to list, find and rent accommodations. This website is an online market place that helps the "Hosts" connect with the "Guests" and vice versa. AirBnb does not focus on scaling their inventory, but concentrates on increasing the number of hosts and guests by matching them with each other. In 2008, the company went a step ahead by pulling data from social networking websites like Facebook to improve their connections.

To enhance the user experience, the company has now come up with a unique solution where the aim is to predict the probable destination a user would want to travel to. The new users on Airbnb can book an accommodation in 34,000 or above cities across 190 or more countries across the world.

REQUIRED DATASET

Our dataset is a collection of 1047891 instances and 14 fields including the class attribute. All these attributes are of type Nominal.

The following table contains the description of these attributes:

Attribute Name	Descriptions
Gender	Male, Female, Others
Age	<25, 25-40, 41-55, >56
Signup method	Basic, Facebook
Signup flow	The page the user came to sign-up up from
Language	English, Others
Affliliate_channel	What kind of paid marketing (Direct, Others)
Affliliate_provider	Where the marketing is (Direct, search and Other)
First_affiliate_tracked	What is the first marketing the user interacted with before signing up
Signup_app	Android, iOS, Moweb, Web
First_device_type	Desktop, Phone, Tablet and Unknown
First_Browser	Chrome, Safari etc.
Action_type	Web session log data (click, view, data, submit and others)
Device_type	Desktop, Phone, Tablet and Unknown
Country_destination	North America and Other

This should be my Final Output. You may want to consider the "similarity" between different cities based on geography, weather, environment, etc.

PLAN TO COLLECT THE DATASET

The dataset is available inKaggle's website under the Competitions sub-section. We can download the data consisting of the training set and test set which are split by dates. The test set consists of predictions about all the new users with first activities after 7/1/2014.We have thesessions dataset, which only dates to 1/1/2014. We also have, the user's dataset consisting of dates way back in 2010 too. We have to perform the necessary cleaning steps to attain a clean dataset.

POTENTIAL IMPLICATIONS OF THE EXPECTED RESULTS

By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community. They want to decrease the average time of first booking, and better forecast demand.

Every user will have a profile that can include recommendations and reviews by other users. It also has a private text and rating system. This is done based on the user's previous bookings and data history.

Reference no: EM131469777

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