Kaggle项目 - Hotel Booking Demand

%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.rc("font",family="SimHei",size="12")  #用于解决中文显示不了的问题
sns.set_style("whitegrid") 

Exploratory data analysis (EDA) 探索性数据分析

一、Data Clean 数据清洗

1.1对空值、NA进行处理

data = pd.read_csv('hotel_booking_demand.csv')
data.head()

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	No Deposit	NaN	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	No Deposit	304.0	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	No Deposit	240.0	NaN	0	Transient	98.0	0	1	Check-Out	2015-07-03

5 rows × 32 columns

data.info()

RangeIndex: 119390 entries, 0 to 119389
Data columns (total 32 columns):
hotel                             119390 non-null object
is_canceled                       119390 non-null int64
lead_time                         119390 non-null int64
arrival_date_year                 119390 non-null int64
arrival_date_month                119390 non-null object
arrival_date_week_number          119390 non-null int64
arrival_date_day_of_month         119390 non-null int64
stays_in_weekend_nights           119390 non-null int64
stays_in_week_nights              119390 non-null int64
adults                            119390 non-null int64
children                          119386 non-null float64
babies                            119390 non-null int64
meal                              119390 non-null object
country                           118902 non-null object
market_segment                    119390 non-null object
distribution_channel              119390 non-null object
is_repeated_guest                 119390 non-null int64
previous_cancellations            119390 non-null int64
previous_bookings_not_canceled    119390 non-null int64
reserved_room_type                119390 non-null object
assigned_room_type                119390 non-null object
booking_changes                   119390 non-null int64
deposit_type                      119390 non-null object
agent                             103050 non-null float64
company                           6797 non-null float64
days_in_waiting_list              119390 non-null int64
customer_type                     119390 non-null object
adr                               119390 non-null float64
required_car_parking_spaces       119390 non-null int64
total_of_special_requests         119390 non-null int64
reservation_status                119390 non-null object
reservation_status_date           119390 non-null object
dtypes: float64(4), int64(16), object(12)
memory usage: 29.1+ MB

data.isnull().sum()  #空值计数

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64

• 缺失值处理方法：可以删除，可以零填充，均值填充，众数填充等
• 从上述结果可以看到children、country、agent、company都有缺失值，company甚至达到112593，总行才119390
• 对四种缺失值进行判断处理：
  • comapny缺失太多，删除
  • children很少，用众数填充
  • country国家众数填充
  • agent旅游机构考虑0填充，代表不属于任何机构

df = data.copy()
df.drop(['company'],axis=1,inplace=True)
df['agent'].fillna(0,inplace=True)
df['children'].fillna(df['children'].mode()[0],inplace=True)
df['country'].fillna(df['country'].mode()[0],inplace=True)

df.isnull().sum()

hotel                             0
is_canceled                       0
lead_time                         0
arrival_date_year                 0
arrival_date_month                0
arrival_date_week_number          0
arrival_date_day_of_month         0
stays_in_weekend_nights           0
stays_in_week_nights              0
adults                            0
children                          0
babies                            0
meal                              0
country                           0
market_segment                    0
distribution_channel              0
is_repeated_guest                 0
previous_cancellations            0
previous_bookings_not_canceled    0
reserved_room_type                0
assigned_room_type                0
booking_changes                   0
deposit_type                      0
agent                             0
days_in_waiting_list              0
customer_type                     0
adr                               0
required_car_parking_spaces       0
total_of_special_requests         0
reservation_status                0
reservation_status_date           0
dtype: int64

• 再次检测，确认数据没有NA值，清洗完成

1.2 异常值处理：

• 客户数量是0的剔除，即adult+children+babies=0的情况
• 查看其他极大极小值

df = df.eval('total_person = babies+children+adults')
df = df[df['total_person'] > 0]
(df.total_person==0).value_counts() #确认剔除干净

False    119210
Name: total_person, dtype: int64

df.plot(legend=False) #快速查看异常值，发现一个adr异常值

df[df.adr>1000] #查找到1条异常值，adr高达5400，删除这条异常值
df = df[~(df.adr>1000)]

1.3 数据整理

• 字段说明文件中提到SC/undefined是同一类型，所以对meal列进行处理
• 把月份更换成数字，方便按照顺序排列

df['meal'].replace('SC','Undefined',inplace=True)

df['arrival_date_month'] = df['arrival_date_month'].map({'January':1, 'February': 2, 'March':3, 'April':4, 'May':5, 'June':6, 'July':7,
                                                            'August':8, 'September':9, 'October':10, 'November':11, 'December':12})

二、Data Analysis 数据分析

1) 对比两家酒店订单取消的情况

1. 堆积条形图，对比两家酒店的取消情况

new = df[['hotel','is_canceled','adr']] #adr列是为了计数，任意列都可以
new.tail()

	hotel	is_canceled	adr
119385	City Hotel	0	96.14
119386	City Hotel	0	225.43
119387	City Hotel	0	157.71
119388	City Hotel	0	104.40
119389	City Hotel	0	151.20

pivot_df = pd.pivot_table(new,values='adr',index='hotel',columns='is_canceled',aggfunc='count')
pivot_df

is_canceled	0	1
hotel
City Hotel	46084	33078
Resort Hotel	28927	11120

pivot_df.plot.bar(stacked=True,color=['tomato','c']) #stacked默认False，并列条形图，True改为堆积条形图，方便对比总数
plt.xticks(rotation=30) #x轴参数倾斜30度显示

(array([0, 1]), )

• 从上述堆积条形图可以看出City Hotel的整体预定量远大于Resort Hotel，取消比例也大于Resort Hotel

2. 再增加一个因变量，查看新客老客取消对比

sns.countplot(x='is_canceled',hue='is_repeated_guest',data=df,palette='Set2')
plt.show()

• 回头客数量仅有很少一部分，新客取消概率更高
• 如果有行业平均数值，或者历史同期数值，对比分析的话意义更大

2) 查看不同的细分市场的取消比率

plt.figure(figsize=(10,6))
sns.countplot(x='market_segment',hue='is_canceled',data=df,palette='Set2')
plt.title('Cancellation of different segment')
plt.xticks(rotation=30)
plt.show()

• 可以看到团队预订Groups取消概率最大，其次是线上旅行团Online TA，次之线下旅行团offline TA/TO

3) 查看不同月份的取消比率

# help(sns.barplot)

sns.barplot('arrival_date_month',y='is_canceled',data=df)
plt.xticks(rotation=30)
plt.show()

4) 不同房型的费用

### 注意在开始之前，要先剔除无效订单，即选择预订成功的数据is_canceled为0的数据
df_valid = df[df.is_canceled==0]

plt.figure(figsize=(10,6))
sns.boxplot(x='reserved_room_type',y='adr',hue='hotel',data=df_valid,palette='Set2')
plt.title('Different Room Price')
plt.ylim(0, 400)
plt.show()

• 箱型图可以直观的看出异常值，和大部分数据的分布情况；

5) 查看不同月份的订单量

group_df = df_valid.groupby(['arrival_date_month','hotel']).count().reset_index()
group_df.head()

	arrival_date_month	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date	total_person
0	1	City Hotel	2249	2249	2249	2249	2249	2249	2249	2249	...	2249	2249	2249	2249	2249	2249	2249	2249	2249	2249
1	1	Resort Hotel	1866	1866	1866	1866	1866	1866	1866	1866	...	1866	1866	1866	1866	1866	1866	1866	1866	1866	1866
2	2	City Hotel	3051	3051	3051	3051	3051	3051	3051	3051	...	3051	3051	3051	3051	3051	3051	3051	3051	3051	3051
3	2	Resort Hotel	2308	2308	2308	2308	2308	2308	2308	2308	...	2308	2308	2308	2308	2308	2308	2308	2308	2308	2308
4	3	City Hotel	4049	4049	4049	4049	4049	4049	4049	4049	...	4049	4049	4049	4049	4049	4049	4049	4049	4049	4049

5 rows × 32 columns

city_df = group_df[group_df.hotel == 'City Hotel']
Resort_df = group_df[group_df.hotel == 'Resort Hotel']

plt.figure(figsize=(10,6))
plt.title('Monthly_Customers')
plt.plot(Resort_df.arrival_date_month,Resort_df.is_canceled,label='Resort Hotel')
plt.plot(city_df.arrival_date_month,city_df.is_canceled,label='City Hotel')
plt.legend()
plt.show()

• 两种酒店的订单趋势不同，经营模式不同
• 城市酒店3至10月都很高，订单量在4000以上，而11至2月订单较低，分析原因是这段时间国外有很多节日，万圣节/圣诞节/新年，所以出门旅行的概率较低
• 度假酒店趋势更稳定一些，整体上也是3至10较高，在2500单左右浮动，不过6月/9月有一个低谷，降到2000单左右
• 条形图更简单，下面是另一种对比方法

plt.figure(figsize=(10,6))
bar = sns.countplot(x='arrival_date_month',hue='hotel',data=df_valid,palette='Set2') #使用有效订单数据
plt.title('Monthly_Customers')
plt.show()

6) 查看不同月份的人均消费价格，是否会随着订单量而变动

• 首先定义人均价格算法，ADR=客房收入/实际售出客房数量，已售客房平均房价；其中数据源中有3列人员，婴儿/儿童/成人，此处我们仅计算成人+儿童，婴儿剔除不计人数，利用ADR/人数，得到人均价格；（记得剔除人数为0的情况）

df_valid.loc[:,'avg'] = df_valid['adr'] / (df_valid['total_person'])

df_valid.head()

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	agent	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date	total_person	avg
0	Resort Hotel	0	342	2015	7	27	1	0	0	2	...	0.0	0	Transient	0.0	0	0	Check-Out	2015-07-01	2.0	0.0
1	Resort Hotel	0	737	2015	7	27	1	0	0	2	...	0.0	0	Transient	0.0	0	0	Check-Out	2015-07-01	2.0	0.0
2	Resort Hotel	0	7	2015	7	27	1	0	1	1	...	0.0	0	Transient	75.0	0	0	Check-Out	2015-07-02	1.0	75.0
3	Resort Hotel	0	13	2015	7	27	1	0	1	1	...	304.0	0	Transient	75.0	0	0	Check-Out	2015-07-02	1.0	75.0
4	Resort Hotel	0	14	2015	7	27	1	0	2	2	...	240.0	0	Transient	98.0	0	1	Check-Out	2015-07-03	2.0	49.0

5 rows × 33 columns

df_valid.avg.describe()

count    75011.000000
mean        54.553351
std         28.149583
min         -3.190000
25%         37.200000
50%         49.425000
75%         66.072500
max        510.000000
Name: avg, dtype: float64

plt.figure(figsize=(10,6))
sns.lineplot(x='arrival_date_month',y='avg',hue='hotel',ci='sd',data=df_valid,palette='Set2')
plt.title('Average Price by Month')

Text(0.5, 1.0, 'Average Price by Month')

• 可以看出基本城市酒店的价格大部分时间都高于度假酒店，除了6至8月一段时间是低于
• 城市酒店的平均房价基本在60左右，上下约15%浮动
• 度假酒店的平均房价上下浮动更大，最高出现在8月，最低出现在1/11月，价格之差高达约165%
• 7/8月两种酒店的订单量都达到峰值，而价格只有度假酒店较高，酒店价格反而有下降趋势，分析可能这个时间大家习惯去度假