主题：京东用户购买意向预测

数据集：

这里涉及到的数据集是京东最新的数据集：JData_User.csv 用户数据集 105,321个用户JData_Comment.csv 商品评论 558,552条记录JData_Product.csv 预测商品集合 24,187条记录JData_Action_02.csv 2月份行为交互记录 11,485,424条记录JData_Action_03.csv 3月份行为交互记录 25,916,378条记录JData_Action_04.csv 4月份行为交互记录 13,199,934条记录

数据挖掘流程：

（一）.数据清洗

1. 数据集完整性验证

2. 数据集中是否存在缺失值

3. 数据集中各特征数值应该如何处理

4. 哪些数据是我们想要的，哪些是可以过滤掉的

5. 将有价值数据信息做成新的数据源

6. 去除无行为交互的商品和用户

7. 去掉浏览量很大而购买量很少的用户(惰性用户或爬虫用户)

（二）.数据理解与分析

1. 掌握各个特征的含义

2. 观察数据有哪些特点，是否可利用来建模

3. 可视化展示便于分析

4. 用户的购买意向是否随着时间等因素变化

（三）.特征提取

1. 基于清洗后的数据集哪些特征是有价值

2. 分别对用户与商品以及其之间构成的行为进行特征提取

3. 行为因素中哪些是核心？如何提取？

4. 瞬时行为特征or累计行为特征？

（四）.模型建立

1. 使用机器学习算法进行预测

2. 参数设置与调节

3. 数据集切分？

数据集验证

首先检查JData_User中的用户和JData_Action中的用户是否一致

保证行为数据中的所产生的行为均由用户数据中的用户产生（但是可能存在用户在行为数据中无行为）

def user_action_check():df_user = pd.read_csv('data/JData_User.csv',encoding='gbk')df_sku = df_user.loc[:,'user_id'].to_frame()df_month2 = pd.read_csv('data/JData_Action_02.csv',encoding='gbk')print ('Is action of Feb. from User file? ', len(df_month2) == len(pd.merge(df_sku,df_month2)))df_month3 = pd.read_csv('data/JData_Action_03.csv',encoding='gbk')print ('Is action of Mar. from User file? ', len(df_month3) == len(pd.merge(df_sku,df_month3)))df_month4 = pd.read_csv('data/JData_Action_04.csv',encoding='gbk')print ('Is action of Apr. from User file? ', len(df_month4) == len(pd.merge(df_sku,df_month4)))user_action_check()

Is action of Feb. from User file? True

Is action of Mar. from User file? True

Is action of Apr. from User file? True

结论： User数据集中的用户和交互行为数据集中的用户完全一致

根据merge前后的数据量比对，能保证Action中的用户ID是User中的ID的子集

检查是否有重复记录

除去各个数据文件中完全重复的记录,可能解释是重复数据是有意义的，比如用户同时购买多件商品，同时添加多个数量的商品到购物车等…

def deduplicate(filepath, filename, newpath):df_file = pd.read_csv(filepath,encoding='gbk') before = df_file.shape[0]df_file.drop_duplicates(inplace=True)after = df_file.shape[0]n_dup = before-afterprint ('No. of duplicate records for ' + filename + ' is: ' + str(n_dup))if n_dup != 0:df_file.to_csv(newpath, index=None)else:print ('no duplicate records in ' + filename)

# deduplicate('data/JData_Action_02.csv', 'Feb. action', 'data/JData_Action_02_dedup.csv')deduplicate('data/JData_Action_03.csv', 'Mar. action', 'data/JData_Action_03_dedup.csv')deduplicate('data/JData_Action_04.csv', 'Feb. action', 'data/JData_Action_04_dedup.csv')deduplicate('data/JData_Comment.csv', 'Comment', 'data/JData_Comment_dedup.csv')deduplicate('data/JData_Product.csv', 'Product', 'data/JData_Product_dedup.csv')deduplicate('data/JData_User.csv', 'User', 'data/JData_User_dedup.csv')

No. of duplicate records for Mar. action is: 7085038

No. of duplicate records for Feb. action is: 3672710

No. of duplicate records for Comment is: 0

no duplicate records in Comment

No. of duplicate records for Product is: 0

no duplicate records in Product

No. of duplicate records for User is: 0

no duplicate records in User

df_month2 = pd.read_csv('data/JData_Action_02.csv',encoding='gbk')IsDuplicated = df_month2.duplicated() df_d=df_month2[IsDuplicated]df_d.groupby('type').count()

发现重复数据大多数都是由于浏览（1），或者点击(6)产生

行为数据中的user_id为浮点型，进行INT类型转换

import pandas as pddf_month = pd.read_csv('data\JData_Action_02.csv',encoding='gbk')df_month['user_id'] = df_month['user_id'].apply(lambda x:int(x))print (df_month['user_id'].dtype)df_month.to_csv('data\JData_Action_02.csv',index=None)df_month = pd.read_csv('data\JData_Action_03.csv',encoding='gbk')df_month['user_id'] = df_month['user_id'].apply(lambda x:int(x))print (df_month['user_id'].dtype)df_month.to_csv('data\JData_Action_03.csv',index=None)df_month = pd.read_csv('data\JData_Action_04.csv',encoding='gbk')df_month['user_id'] = df_month['user_id'].apply(lambda x:int(x))print (df_month['user_id'].dtype)df_month.to_csv('data\JData_Action_04.csv',index=None)

年龄区间的处理

import pandas as pddf_user = pd.read_csv('data\JData_User.csv',encoding='gbk')def tranAge(x):if x == u'15岁以下':x='1'elif x==u'16-25岁':x='2'elif x==u'26-35岁':x='3'elif x==u'36-45岁':x='4'elif x==u'46-55岁':x='5'elif x==u'56岁以上':x='6'return xdf_user['age']=df_user['age'].apply(tranAge)print (df_user.groupby(df_user['age']).count())df_user.to_csv('data\JData_User.csv',index=None)

为了能够进行上述清洗,在此首先构造了简单的用户(user)行为特征和商品(item)行为特征,对应于两张表user_table和

user_table

user_table特征包括:user_id(用户id),age(年龄),sex(性别),user_lv_cd(用户级别),browse_num(浏览数),addcart_num(加购数),delcart_num(删购数),buy_num(购买数),favor_num(收藏数),click_num(点击数),buy_addcart_ratio(购买加购转化率),buy_browse_ratio(购买浏览转化率),buy_click_ratio(购买点击转化率),buy_favor_ratio(购买收藏转化率)

item_table特征包括:

sku_id(商品id),attr1,attr2,attr3,cate,brand,browse_num,addcart_num,delcart_num,buy_num,favor_num,click_num,buy_addcart_ratio,buy_browse_ratio,buy_click_ratio,buy_favor_ratio,comment_num(评论数),has_bad_comment(是否有差评),bad_comment_rate(差评率)

构建User_table

#定义文件名ACTION_02_FILE = "data/JData_Action_02.csv"ACTION_03_FILE = "data/JData_Action_03.csv"ACTION_04_FILE = "data/JData_Action_04.csv"COMMENT_FILE = "data/JData_Comment.csv"PRODUCT_FILE = "data/JData_Product.csv"USER_FILE = "data/JData_User.csv"USER_TABLE_FILE = "data/User_table.csv"ITEM_TABLE_FILE = "data/Item_table.csv"

# 导入相关包import pandas as pdimport numpy as npfrom collections import Counter

# 功能函数: 对每一个user分组的数据进行统计def add_type_count(group):behavior_type = group.type.astype(int)# 用户行为类别type_cnt = Counter(behavior_type)# 1: 浏览 2: 加购 3: 删除# 4: 购买 5: 收藏 6: 点击group['browse_num'] = type_cnt[1]group['addcart_num'] = type_cnt[2]group['delcart_num'] = type_cnt[3]group['buy_num'] = type_cnt[4]group['favor_num'] = type_cnt[5]group['click_num'] = type_cnt[6]return group[['user_id', 'browse_num', 'addcart_num','delcart_num', 'buy_num', 'favor_num','click_num']]

由于用户行为数据量较大,一次性读入可能造成内存错误(Memory Error),因而使用pandas的分块(chunk)读取.

#对action数据进行统计#根据自己调节chunk_size大小def get_from_action_data(fname, chunk_size=50000):reader = pd.read_csv(fname, header=0, iterator=True,encoding='gbk')chunks = []loop = Truewhile loop:try:# 只读取user_id和type两个字段chunk = reader.get_chunk(chunk_size)[["user_id", "type"]]chunks.append(chunk)except StopIteration:loop = Falseprint("Iteration is stopped")`# 将各个action数据的统计量进行聚合def merge_action_data():df_ac = []df_ac.append(get_from_action_data(fname=ACTION_02_FILE))df_ac.append(get_from_action_data(fname=ACTION_03_FILE))df_ac.append(get_from_action_data(fname=ACTION_04_FILE))df_ac = pd.concat(df_ac, ignore_index=True)# 用户在不同action表中统计量求和df_ac = df_ac.groupby(['user_id'], as_index=False).sum()#构造转化率字段df_ac['buy_addcart_ratio'] = df_ac['buy_num'] / df_ac['addcart_num']df_ac['buy_browse_ratio'] = df_ac['buy_num'] / df_ac['browse_num']df_ac['buy_click_ratio'] = df_ac['buy_num'] / df_ac['click_num']df_ac['buy_favor_ratio'] = df_ac['buy_num'] / df_ac['favor_num']# 将大于１的转化率字段置为１(100%)df_ac.ix[df_ac['buy_addcart_ratio'] > 1., 'buy_addcart_ratio'] = 1.df_ac.ix[df_ac['buy_browse_ratio'] > 1., 'buy_browse_ratio'] = 1.df_ac.ix[df_ac['buy_click_ratio'] > 1., 'buy_click_ratio'] = 1.df_ac.ix[df_ac['buy_favor_ratio'] > 1., 'buy_favor_ratio'] = 1.return df_ac`# 将块拼接为pandas dataframe格式df_ac = pd.concat(chunks, ignore_index=True)# 按user_id分组，对每一组进行统计，as_index 表示无索引形式返回数据df_ac = df_ac.groupby(['user_id'], as_index=False).apply(add_type_count)# 将重复的行丢弃df_ac = df_ac.drop_duplicates('user_id')return df_ac

#从FJData_User表中抽取需要的字段def get_from_jdata_user():df_usr = pd.read_csv(USER_FILE, header=0)df_usr = df_usr[["user_id", "age", "sex", "user_lv_cd"]]return df_usr

user_base = get_from_jdata_user()user_behavior = merge_action_data()

# 连接成一张表，类似于SQL的左连接(left join)user_behavior = pd.merge(user_base, user_behavior, on=['user_id'], how='left')# 保存为user_table.csvuser_behavior.to_csv(USER_TABLE_FILE, index=False)

user_table = pd.read_csv(USER_TABLE_FILE)user_table.head()

构建Item_table

#定义文件名ACTION_02_FILE = "data/JData_Action_02.csv"ACTION_03_FILE = "data/JData_Action_03.csv"ACTION_04_FILE = "data/JData_Action_04.csv"COMMENT_FILE = "data/JData_Comment.csv"PRODUCT_FILE = "data/JData_Product.csv"USER_FILE = "data/JData_User.csv"USER_TABLE_FILE = "data/User_table.csv"ITEM_TABLE_FILE = "data/Item_table.csv"

# 导入相关包import pandas as pdimport numpy as npfrom collections import Counter

# 读取Product中商品def get_from_jdata_product():df_item = pd.read_csv(PRODUCT_FILE, header=0,encoding='gbk')return df_item

# 对每一个商品分组进行统计def add_type_count(group):behavior_type = group.type.astype(int)type_cnt = Counter(behavior_type)group['browse_num'] = type_cnt[1]group['addcart_num'] = type_cnt[2]group['delcart_num'] = type_cnt[3]group['buy_num'] = type_cnt[4]group['favor_num'] = type_cnt[5]group['click_num'] = type_cnt[6]return group[['sku_id', 'browse_num', 'addcart_num','delcart_num', 'buy_num', 'favor_num','click_num']]

#对action中的数据进行统计def get_from_action_data(fname, chunk_size=50000):reader = pd.read_csv(fname, header=0, iterator=True)chunks = []loop = Truewhile loop:try:chunk = reader.get_chunk(chunk_size)[["sku_id", "type"]]chunks.append(chunk)except StopIteration:loop = Falseprint("Iteration is stopped")df_ac = pd.concat(chunks, ignore_index=True)df_ac = df_ac.groupby(['sku_id'], as_index=False).apply(add_type_count)# Select unique rowdf_ac = df_ac.drop_duplicates('sku_id')return df_ac

# 获取评论中的商品数据,如果存在某一个商品有两个日期的评论，我们取最晚的那一个def get_from_jdata_comment():df_cmt = pd.read_csv(COMMENT_FILE, header=0)df_cmt['dt'] = pd.to_datetime(df_cmt['dt'])# find latest comment indexidx = df_cmt.groupby(['sku_id'])['dt'].transform(max) == df_cmt['dt']df_cmt = df_cmt[idx]return df_cmt[['sku_id', 'comment_num','has_bad_comment', 'bad_comment_rate']]

def merge_action_data():df_ac = []df_ac.append(get_from_action_data(fname=ACTION_02_FILE))df_ac.append(get_from_action_data(fname=ACTION_03_FILE))df_ac.append(get_from_action_data(fname=ACTION_04_FILE))df_ac = pd.concat(df_ac, ignore_index=True)df_ac = df_ac.groupby(['sku_id'], as_index=False).sum()df_ac['buy_addcart_ratio'] = df_ac['buy_num'] / df_ac['addcart_num']df_ac['buy_browse_ratio'] = df_ac['buy_num'] / df_ac['browse_num']df_ac['buy_click_ratio'] = df_ac['buy_num'] / df_ac['click_num']df_ac['buy_favor_ratio'] = df_ac['buy_num'] / df_ac['favor_num']df_ac.ix[df_ac['buy_addcart_ratio'] > 1., 'buy_addcart_ratio'] = 1.df_ac.ix[df_ac['buy_browse_ratio'] > 1., 'buy_browse_ratio'] = 1.df_ac.ix[df_ac['buy_click_ratio'] > 1., 'buy_click_ratio'] = 1.df_ac.ix[df_ac['buy_favor_ratio'] > 1., 'buy_favor_ratio'] = 1.return df_ac

item_base = get_from_jdata_product()item_behavior = merge_action_data()item_comment = get_from_jdata_comment()# SQL: left joinitem_behavior = pd.merge(item_base, item_behavior, on=['sku_id'], how='left')item_behavior = pd.merge(item_behavior, item_comment, on=['sku_id'], how='left')item_behavior.to_csv(ITEM_TABLE_FILE, index=False)

item_table = pd.read_csv(ITEM_TABLE_FILE)item_table.head()

用户清洗

import pandas as pddf_user = pd.read_csv('data/User_table.csv',header=0)pd.options.display.float_format = '{:,.3f}'.format #输出格式设置，保留三位小数df_user.describe()

由上述统计信息发现： 第一行中根据User_id统计发现有105321个用户，发现有3个用户没有age,sex字段，而且根据浏览、加购、删购、购买等记录却只有105180条记录，说明存在用户无任何交互记录，因此可以删除上述用户。

删除没有age,sex字段的用户

df_user[df_user['age'].isnull()]delete_list = df_user[df_user['age'].isnull()].indexdf_user.drop(delete_list,axis=0,inplace=True)

删除无交互记录的用户

#删除无交互记录的用户df_naction = df_user[(df_user['browse_num'].isnull()) & (df_user['addcart_num'].isnull()) & (df_user['delcart_num'].isnull()) & (df_user['buy_num'].isnull()) & (df_user['favor_num'].isnull()) & (df_user['click_num'].isnull())]df_user.drop(df_naction.index,axis=0,inplace=True)print (len(df_user))

统计并删除无购买记录的用户

#统计无购买记录的用户df_bzero = df_user[df_user['buy_num']==0]#输出购买数为0的总记录数print (len(df_bzero))

#删除无购买记录的用户df_user = df_user[df_user['buy_num']!=0]

删除爬虫及惰性用户

由上表所知，浏览购买转换比和点击购买转换比均值为0.018,0.030，因此这里认为浏览购买转换比和点击购买转换比小于0.0005的用户为惰性用户

bindex = df_user[df_user['buy_browse_ratio']<0.0005].indexprint (len(bindex))df_user.drop(bindex,axis=0,inplace=True)

cindex = df_user[df_user['buy_click_ratio']<0.0005].indexprint (len(cindex))df_user.drop(cindex,axis=0,inplace=True)

最后这29070个用户为最终预测用户数据集