kaggle—酒店预订需求预测分析（Hotel booking demand）

项目背景：该项目为酒店线上预订业务的研究内容，从酒店运营的角度，分析酒店的房型供给、不同时间段的需求，核心消费群体，影响退订的因素，并建立分类算法模型对酒店订单退订进行预测。

数据来源：kaggle:Hotel booking demand，此项目数据为kaggle上的一个Hotel booking数据集，感兴趣的朋友可以去下载进行练习。

数据介绍：

一共包含32个字段，119390条记录。

项目流程

• 数据预处理
  • 缺失值处理
  • 数据类型转换
  • 异常值处理
• 特征工程
  • 数值型特征标准化
  • 类别型特征 one-hot编码
  • 特征选择
• 模型训练
• 模型预测与评估

一、数据预处理

1. 导入需要的库

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import seaborn as sns

2. 查看并理解数据

df = pd.read_csv('hotel_bookings.csv',encoding='gbk')
df.head()
df.info()

结果：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119390 entries, 0 to 119389
Data columns (total 32 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   hotel                           119390 non-null  object 
 1   is_canceled                     119390 non-null  int64  
 2   lead_time                       119390 non-null  int64  
 3   arrival_date_year               119390 non-null  int64  
 4   arrival_date_month              119390 non-null  object 
 5   arrival_date_week_number        119390 non-null  int64  
 6   arrival_date_day_of_month       119390 non-null  int64  
 7   stays_in_weekend_nights         119390 non-null  int64  
 8   stays_in_week_nights            119390 non-null  int64  
 9   adults                          119390 non-null  int64  
 10  children                        119386 non-null  float64
 11  babies                          119390 non-null  int64  
 12  meal                            119390 non-null  object 
 13  country                         118902 non-null  object 
 14  market_segment                  119390 non-null  object 
 15  distribution_channel            119390 non-null  object 
 16  is_repeated_guest               119390 non-null  int64  
 17  previous_cancellations          119390 non-null  int64  
 18  previous_bookings_not_canceled  119390 non-null  int64  
 19  reserved_room_type              119390 non-null  object 
 20  assigned_room_type              119390 non-null  object 
 21  booking_changes                 119390 non-null  int64  
 22  deposit_type                    119390 non-null  object 
 23  agent                           103050 non-null  float64
 24  company                         6797 non-null    float64
 25  days_in_waiting_list            119390 non-null  int64  
 26  customer_type                   119390 non-null  object 
 27  adr                             119390 non-null  float64
 28  required_car_parking_spaces     119390 non-null  int64  
 29  total_of_special_requests       119390 non-null  int64  
 30  reservation_status              119390 non-null  object 
 31  reservation_status_date         119390 non-null  object 
dtypes: float64(4), int64(16), object(12)
memory usage: 29.1+ MB

发现数据集一共有32个字段，119389行数据，company列有比较明显缺失值，另外arrival_date等表示时间的列需要合并并且转换为日期格式。

3. 日期合并及格式转换

由于数据集中的arrival_date_month月份信息为英文表示，先将其转换为中文月份表示，方便后期合并日期

#修改arrival_date_month的英文月份为中文月份
import calendar
month = []
for i in df.arrival_date_month:
    mon = list(calendar.month_name).index(i)
    month.append(mon)
df.insert(4,"arrival_month",month)

新增一列预订到店的年月日arrival_date,讲原来的年月日拼接

#将年月日拼接
#增加一列预订到店的年月日arrival_date
df[["arrival_date_year","arrival_month","arrival_date_day_of_month"]] = df[["arrival_date_year","arrival_month","arrival_date_day_of_month"]].apply(lambda x:x.astype(str))
date = df.arrival_date_year.str.cat([df.arrival_month,df.arrival_date_day_of_month],".")
df.insert(3,"arrival_date",date)

转换为日期格式

# 转换日期格式
df['arrival_date']=pd.to_datetime(df['arrival_date'])

将原来的年月日信息删除，只采用新建立的arrival_date表示

df.drop(['arrival_date_year','arrival_month','arrival_date_month','arrival_date_week_number'],axis=1,inplace=True)
df.drop(['arrival_date_day_of_month'],axis=1,inplace=True)

4. 缺失值处理

#统计缺失值
df.isnull().sum()
#统计缺失率
#df.isnull().sum()/df.shape[0]

结果：
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

数据的缺失值主要存在于children,country,agent,company4个字段中，缺失最多的是company

一，children缺失4个，且为数值型变量，所以用中位数填充

二，country缺失488个，且为类别型变量，所以使用众数填充

三，agent缺失16340个，缺失率为13.6%，缺失数量较大，但agent表示预订的旅行社，且缺失率小于20%，建议保留，并用0填充，表示没有旅行社ID

四，company缺失112593个，缺失率为94.3%>80%,不具备信息价值有效性，所以直接删除

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

5. 异常值处理

通过观察数据集发现，小孩的入住量，旅行社的入住量存在浮点数，数据集中成人，小孩，婴儿字段均为0，即表示该订单入住人数为0，不符合实际。酒店的平均每日消费存在一个大于5000的异常值。我们需要对此进行处理，以免影响到后续的模型建立。

# children、agent字段不可能为浮点数，需修改数据类型
df.children = df.children.astype(int)
df.agent = df.agent.astype(int)
# 根据原数据集介绍，餐饮字段中的Undefined / SC –无餐套餐为一类
df.meal.replace("Undefined", "SC", inplace=True)
#删除异常值的行
zero_guests = list(df["adults"] + df["children"] + df["babies"] == 0)
df.drop(df.index[zero_guests],inplace=True)
#核实adr变量的离群值情况
sns.boxplot(x=df['adr'])
#删除离群值
df = df[df["adr"]<5000]

特征工程

数值型特征标准化处理

由于数值型特征的单位量纲均不一样，模型拟合时容易偏拟合，所以需要做归一化处理，统一量纲，并保留数据规律

1. 先将数值型特征提取出来

#数值型特征标准化过程
num_feature = ["lead_time","stays_nights_total","stays_in_weekend_nights","stays_in_week_nights","number_of_people","adults","children","babies","is_repeated_guest","previous_cancellations","previous_bookings_not_canceled","booking_changes","agent","days_in_waiting_list","adr","required_car_parking_spaces","total_of_special_requests"]

2. 对数值特征进行标准化处理

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
#df = sc_X.fit_transform(df)
dff=sc_X.fit_transform(df[["lead_time","stays_in_weekend_nights","stays_in_week_nights","adults","children","babies","is_repeated_guest","previous_cancellations","previous_bookings_not_canceled","booking_changes","agent","days_in_waiting_list","adr","required_car_parking_spaces","total_of_special_requests"]])

dff=pd.DataFrame(data=dff, columns=["lead_time","stays_in_weekend_nights","stays_in_week_nights","adults","children","babies","is_repeated_guest","previous_cancellations","previous_bookings_not_canceled","booking_changes","agent","days_in_waiting_list","adr","required_car_parking_spaces","total_of_special_requests"])

类别特征向量化

由于计算机只能识别数值，而不能识别字符串类别信息，所以为了保证信息的完整性，我们需要进行向量化处理，将其转换为模型容易识别的数值型特征

1. 提取类别型特征

cat_feature = ["hotel","meal","country","market_segment","distribution_channel","reserved_room_type","assigned_room_type","deposit_type","customer_type"]

2. one-hot编码

from sklearn.preprocessing import OneHotEncoder

one_hot=OneHotEncoder()

data_temp=pd.DataFrame(one_hot.fit_transform(df[["hotel","meal","country","market_segment","distribution_channel","reserved_room_type","assigned_room_type","deposit_type","customer_type"]]).toarray(),
             columns=one_hot.get_feature_names(["hotel","meal","country","market_segment","distribution_channel","reserved_room_type","assigned_room_type","deposit_type","customer_type"]),dtype='int32')
data_onehot=pd.concat((dff,data_temp),axis=1)    #也可以用merge,join

data_onehot.head()
data_onehot['is_canceled'] = df['is_canceled']

降维

在对类别型特征进行one-hot编码后，数据集由原来的32个字段改变为239个字段，维度大大增加，增加了模型训练的时间复杂性以及可能会造成数据分布稀疏的问题。为了更好的训练模型而又尽量不损失太多的数据信息，在此我们使用决策树模型进行特征选择，保留30个特征进行训练。

# 适用于分类模型
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_selection import RFE
from sklearn.feature_selection import SelectFromModel


def descTree(x,y,n):
    # 数据集划分为特征X和标签y,y是分类
    X, y = x, y
    # 决策树模型
    print('使用决策树模型')
    tree = DecisionTreeClassifier().fit(X, y)
    model1 = SelectFromModel(tree, prefit=True, max_features=n)
    d = model1.get_support(indices=True)
    print('特征是...')
    print(d)
    return d 

d= descTree(x,y,30)
all_fea = pd.DataFrame(d)

结果：
使用决策树模型
特征是...
[  0   1   2   3   4   7   8   9  10  11  12  13  14  16  17  19  20  64
  72  77  80 156 204 211 214 220 223 232 236 237]

进行特征选择后，我们拿到了所选择的30个特征的索引，我们需要对其进行挑选合并处理做最后的训练数据

fea_num = list(d)
data_stand_fea = x.iloc[:, list(fea_num[:])]
data_stand_fea

模型训练

1. 切割数据集

#切割数据集  82开
X_train, X_test, y_train, y_test = train_test_split(data_stand_fea, y, test_size=0.2)

2. 采用RandomForest模型进行训练

clf3 = RandomForestClassifier(n_estimators=160,
                               max_features=0.4,
                               min_samples_split=2,
                               n_jobs=-1,
                               random_state=0)
clf3.fit(X_train,y_train)

3. 模型预测与评估

from sklearn.metrics import accuracy_score
y_pred3 = clf3.predict(X_test)
print('The accuracy of prediction is:', accuracy_score(y_test, y_pred3))

随机森林的评分为0.83，作为第一次训练的结果，评分还是不错的，后续我们可以进行模型参数调优或者采用更加复杂的模型进行训练，提高预测精度。在此我们对此进行一个简单的参数调优

4. 参数调优

参数调优可以使用GridSearchCV，但在参数数量选择上，不建议太多，否则数据处理量太多，速度会很慢。对应该模型，参数选择”n_estimators”：决策树的量；“max_depth”：决策树的深度（预剪枝）；“max_features”：选择的最大特征量

rf = RandomForestClassifier()
#参数选择
param_dict = {"n_estimators":[100,150,200],"max_depth":[3,5,8,10,15],"max_features":["auto","log2"]}
#网络搜索调优器
rf_model = GridSearchCV(rf,param_grid=param_dict,cv=3)
#模型拟合
CLF = Pipeline(steps=[('preprocessor', preprocessor),
                      ('classifier', rf_model)])
CLF.fit(X_train, y_train)
#不同参数下，最好的评分及其参数
CLF.best_score_
CLF.best_params_