Kaggle's Airbnb New User Booking Trend

2015/12/29 · 438 minute read
d3   data-vis   deep-learning   kaggle   machine-learning   python   timeseries

Using mpld3 to do visualization in ipython with Kaggle’s airbnb data. First experience is great!

Datasource

Kaggle Description: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings

In [1]:
# load pkg
import datetime
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import mpld3
mpld3.enable_notebook()

# import mod
%autoreload 1
%aimport mod

# save data in one hdf
data = ['train_users_2', 'age_gender_bkts', 'countries', 'sessions', 'test_users', 'sample_submission_NDF']
data_name = ['train', 'age_gender', 'countries', 'sessions', 'test', 'submissions']

def read_data(data, data_name):
    hdf = pd.HDFStore('data.h5')
    for di in range(len(data)):
        fn = data[di]
        dn = data_name[di]
        hdf.put(dn, pd.read_csv('DATA/%s.csv'%fn))

# read_data(data, data_name)
        
# timer
now = datetime.datetime.now()

# read data from hdf
hdf = pd.HDFStore('data.h5')
train = hdf.train
sessions = hdf.sessions
age_gender = hdf.age_gender
countries = hdf.countries

print('data loaded, took %i seconds' %(datetime.datetime.now() - now).seconds)

# transform timestamp columns to datetime
train.date_account_created = pd.to_datetime(train.date_account_created) 
train.timestamp_first_active = pd.to_datetime(train.timestamp_first_active, format = '%Y%m%d%H%M%S')
train.date_first_booking = pd.to_datetime(train.date_first_booking)

# take a look
train.head()


data loaded, took 4 seconds
Out[1]:
id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 2009-03-19 04:32:55 NaT -unknown- NaN facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 2009-05-23 17:48:09 NaT MALE 38 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 2009-06-09 23:12:47 2010-08-02 FEMALE 56 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 2009-10-31 06:01:29 2012-09-08 FEMALE 42 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 2009-12-08 06:11:05 2010-02-18 -unknown- 41 basic 0 en direct direct untracked Web Mac Desktop Chrome US

Profiling

date_first_booking

In [10]:
mod.draw_trend(train, ‘doy’);

In [11]:
mod.draw_trend(train, ‘doy’, var = ‘timestamp_first_active’);

In [12]:
mod.draw_trend(train, ’m’)

In [13]:
mod.draw_trend(train, ’m’, var = ‘timestamp_first_active’)

In [14]:
mod.draw_trend(train, ’d’);

In [15]:
mod.draw_trend(train, ‘dow’)

In [16]:
mod.draw_trend(train, ‘dow’, var = ‘timestamp_first_active’)

In [17]:
mod.draw_trend(train, ‘hr’, var = ‘timestamp_first_active’)

In [20]:
mod.draw_trend(train, ‘country_destination’, var = ‘timestamp_first_active’)

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