Air BnB Project Description

What are we trying to accomplish?
	Based on data available through Air BnB predict which country a new user's first booking destination will be

What data is available?
	List of users:
		demographics
		web session records
		some summary statistics

	Training data:
		User data
		User's first booking destiniation 
		There are 12 possible outcomes of the destination country:
			'US',
			'FR',
			'CA',
			'GB',
			'ES',
			'IT',
			'PT',
			'NL',
			'DE',
			'AU',
			'NDF' (no destination found, implying they did not book),
		and 'other'.


File descriptions

	train_users.csv - the training set of users
	test_users.csv - the test set of users
		id: user id
		date_account_created: the date of account creation
		timestamp_first_active: timestamp of the first activity, note that it can be earlier than date_account_created or date_first_booking because a user can search before signing up
		date_first_booking: date of first booking
		gender
		age
		signup_method
		signup_flow: the page a user came to signup up from
		language: international language preference
		affiliate_channel: what kind of paid marketing
		affiliate_provider: where the marketing is e.g. google, craigslist, other
		first_affiliate_tracked: whats the first marketing the user interacted with before the signing up
		signup_app
		first_device_type
		first_browser
		country_destination: this is the target variable you are to predict

	sessions.csv - web sessions log for users
		user_id: to be joined with the column 'id' in users table
		action
		action_type
		action_detail
		device_type
		secs_elapsed

	countries.csv - summary statistics of destination countries in this dataset and their locations

	age_gender_bkts.csv - summary statistics of users' age group, gender, country of destination

	sample_submission.csv - correct format for submitting your predictions