一、数据采集（selenium）
from selenium import webdriver
import time
import re
import pandas as pd
import os
** 在爬取的过程中可能会有登陆弹窗，要先定义一个处理弹窗的函数**
def close_windows():
#如果有登录弹窗，就关闭
try:
time.sleep(0.5)
if dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon"):
dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon").click()
except BaseException as e:
print('close_windows,没有弹窗',e)
** 爬取部分，这里爬取维度为11列，基本上包含了职位的大部分信息**
def get_current_region_job(k_index):
flag = 0
# page_num_set=0#每区获取多少条数据，对30取整
df_empty = pd.DataFrame(columns=['岗位', '地点', '薪资', '工作经验', '学历', '公司名称', '技能','工作福利','工作类型','融资情况','公司规模'])
while (flag == 0):
# while (page_num_set<151)&(flag == 0):#每次只能获取150条信息
time.sleep(0.5)
close_windows()
job_list = dr.find_elements_by_class_name("job-primary")
for job in job_list:#获取当前页的职位30条
job_name = job.find_element_by_class_name("job-name").text
# print(job_name)
job_area = job.find_element_by_class_name("job-area").text
# salary = job.find_element_by_class_name("red").get_attribute("textContent") # 获取薪资
salary_raw = job.find_element_by_class_name("red").get_attribute("textContent") # 获取薪资
salary_split = salary_raw.split('·') # 根据·分割
salary = salary_split[0] # 只取薪资，去掉多少薪
# if re.search(r'天', salary):
# continue
experience_education = job.find_element_by_class_name("job-limit").find_element_by_tag_name(
"p").get_attribute("innerHTML")
# experience_education_raw = '1-3年<em class="vline"></em>本科'
experience_education_raw = experience_education
split_str = re.search(r'[a-zA-Z =<>/"]{23}', experience_education_raw) # 搜索分割字符串<em class="vline"></em>
# print(split_str)
experience_education_replace = re.sub(r'[a-zA-Z =<>/"]{23}', ",", experience_education_raw) # 分割字符串替换为逗号
# print(experience_education_replace)
experience_education_list = experience_education_replace.split(',') # 根据逗号分割
# print('experience_education_list:',experience_education_list)
if len(experience_education_list)!=2:
print('experience_education_list不是2个，跳过该数据',experience_education_list)
break
experience = experience_education_list[0]
education = experience_education_list[1]
# print(experience)
# print(education)
company_type = job.find_element_by_class_name("company-text").find_element_by_tag_name(
"p").get_attribute("innerHTML")
company_type_size_row=company_type
split_str_2=re.search(r'[a-zA-Z =<>/"]{23}', company_type_size_row)
# print(split_str_2)
# print("split2------------------------------------------------------")
company_size_replace= re.sub(r'[a-zA-Z =<>/"]{23}', ",", company_type_size_row)
# print(company_size_replace)
company_size_list=company_size_replace.split(',')
# print(company_size_list)
if len(company_size_list) != 3:
print('company_size_list不是3个，跳过该数据', company_size_list)
break
company_direct_info = company_size_list[0].split(">")[1]
company_salary_info = company_size_list[1].split(">")[1]
company_size_info=company_size_list[2]
company = job.find_element_by_class_name("company-text").find_element_by_class_name("name").text
skill_list = job.find_element_by_class_name("tags").find_elements_by_class_name("tag-item")
skill = []
job_advantage=job.find_element_by_class_name("info-desc").text
for skill_i in skill_list:
skill_i_text = skill_i.text
if len(skill_i_text) == 0:
continue
skill.append(skill_i_text)
# print(job_name)
# print(skill)
df_empty.loc[k_index, :] = [job_name, job_area, salary, experience, education, company, skill,job_advantage,company_direct_info,company_salary_info,company_size_info]
print(df_empty.loc[k_index, :])
k_index = k_index + 1
# page_num_set=page_num_set+1
print("已经读取数据{}条".format(k_index))
close_windows()
try:#点击下一页
cur_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
# print('cur_page_num',cur_page_num)
#点击下一页
element = dr.find_element_by_class_name("page").find_element_by_class_name("next")
dr.execute_script("arguments[0].click();", element)
time.sleep(1)
# print('点击下一页')
new_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
# print('new_page_num',new_page_num)
if cur_page_num==new_page_num:
flag = 1
break
except BaseException as e:
print('点击下一页错误',e)
break
print(df_empty)
if os.path.exists("ai数据.csv"):#存在追加，不存在创建
df_empty.to_csv('ai数据.csv', mode='a', header=False, index=None, encoding='gb18030')
else:
df_empty.to_csv("ai数据.csv", index=False, encoding='gb18030')
return k_index
** 自动化爬取部分 这里按照全国14个热门城市爬取 若想爬取某个固定城市，需要把for循环去掉，去网站上找到对应城市编码，剪贴url即可**
def main():
# 打开浏览器
# dr = webdriver.Firefox()
global dr
dr = webdriver.Chrome()
# dr = webdriver.Ie()
# # 后台打开浏览器
# option=webdriver.ChromeOptions()
# option.add_argument('headless')
# dr = webdriver.Chrome(chrome_options=option)
# print("打开浏览器")
# 将浏览器最大化显示
dr.maximize_window()
# 转到目标网址
dr.get("https://www.zhipin.com/job_detail/?query=人工智能&city=100010000&industry=&position=")#全国
# dr.get("https://www.zhipin.com/c101010100/?query=人工智能&ka=sel-city-101010100")#北京
print("打开网址")
time.sleep(5)
k_index = 0#数据条数、DataFrame索引
flag_hot_city=0
for i in range(3,17,1):
# print('第',i-2,'页')
# try:
# 获取城市
close_windows()
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
close_windows()
# hot_city_list[i].click()#防止弹窗，改为下面两句
# element_hot_city_list_first = hot_city_list[i]
dr.execute_script("arguments[0].click();", hot_city_list[i])
# 输出城市名
close_windows()
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
print('城市：{}'.format(i-2),hot_city_list[i].text)
time.sleep(0.5)
# 获取区县
for j in range(1,50,1):
# print('第', j , '个区域')
# try:
# close_windows()
# hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
# 在这个for循环点一下城市，不然识别不到当前页面已经更新了
close_windows()
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
close_windows()
# hot_city_list[i].click()#防止弹窗，改为下面
dr.execute_script("arguments[0].click();", hot_city_list[i])
#输出区县名称
close_windows()
city_district = dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
if len(city_district)==j:
print('遍历完所有区县，没有不可点击的，跳转下一个城市')
break
print('区县：',j, city_district[j].text)
# city_district_value=city_district[j].text#当前页面的区县值
# 点击区县
close_windows()
city_district= dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
close_windows()
# city_district[j].click()]#防止弹窗，改为下面两句
# element_city_district = city_district[j]
dr.execute_script("arguments[0].click();", city_district[j])
#判断区县是不是点完了
close_windows()
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
print('点击后这里应该是区县', hot_city_list[1].text)#如果是不限，说明点完了，跳出
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
print('如果点完了，这里应该是不限：',hot_city_list[1].text)
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
if hot_city_list[1].text == '不限':
print('当前区县已经点完了，点击下一个城市')
flag_hot_city=1
break
close_windows()
k_index = get_current_region_job(k_index)#获取职位，爬取数据
# 重新点回城市页面，再次获取区县。但此时多了区县，所以i+1
close_windows()
hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
close_windows()
# hot_city_list[i+1].click()#防止弹窗，改为下面两句
# element_hot_city_list_again = hot_city_list[i+1]
dr.execute_script("arguments[0].click();", hot_city_list[i+1])
# except BaseException as e:
# print('main的j循环-获取区县发生错误:', e)
# close_windows()
time.sleep(0.5)
# except BaseException as e:
# print('main的i循环发生错误:',e)
# close_windows()
time.sleep(0.5)
# 退出浏览器
dr.quit()
# p1.close()
最后调用main即可，爬取结果如下 数据量共计一万（人工智能职位）
数据为两部分：分别为全国人工智能职位爬取 热门城市人工职位数据爬取
二、数据预处理（Python）
** 简单做一些缺失值和规范化的处理 具体分析部分在Hive中**
# coding=utf-8
import collections
import wordcloud
import re
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
def create_dir_not_exist(path): # 判断文件夹是否存在,不存在-新建
if not os.path.exists(path):
os.mkdir(path)
create_dir_not_exist(r'./image')
create_dir_not_exist(r'./image/city')
data = pd.read_csv('ai数据.csv', encoding='gb18030')
data_df = pd.DataFrame(data)
print("\n查看是否有缺失值\n", data_df.isnull().sum())
data_df_del_empty = data_df.dropna(subset=['岗位'], axis=0)
# print("\n删除缺失值‘岗位'的整行\n",data_df_del_empty)
data_df_del_empty = data_df_del_empty.dropna(subset=['公司名称'], axis=0)
# print("\n删除缺失值‘公司'的整行\n",data_df_del_empty)
print("\n查看是否有缺失值\n", data_df_del_empty.isnull().sum())
print('去除缺失值后\n', data_df_del_empty)
data_df_python_keyword = data_df_del_empty.loc[data_df_del_empty['岗位'].str.contains('人工智能|AI')]
# print(data_df_python_keyword)#筛选带有python的行
# 区间最小薪资
data_df_python_keyword_salary = data_df_python_keyword['薪资'].str.split('-', expand=True)[0]
print(data_df_python_keyword_salary) # 区间最小薪资
# Dataframe新增一列 在第 列新增一列名为' ' 的一列 数据
data_df_python_keyword.insert(7, '区间最小薪资(K)', data_df_python_keyword_salary)
print(data_df_python_keyword)
# 城市地区
data_df_python_keyword_location_city = data_df_python_keyword['地点'].str.split('·', expand=True)[0]
print(data_df_python_keyword_location_city) # 北京
data_df_python_keyword_location_district = data_df_python_keyword['地点'].str.split('·', expand=True)[1]
print(data_df_python_keyword_location_district) # 海淀区
data_df_python_keyword_location_city_district = []
for city, district in zip(data_df_python_keyword_location_city, data_df_python_keyword_location_district):
city_district = city + district
data_df_python_keyword_location_city_district.append(city_district)
print(data_df_python_keyword_location_city_district) # 北京海淀区
# Dataframe新增一列 在第 列新增一列名为' ' 的一列 数据
data_df_python_keyword.insert(8, '城市地区', data_df_python_keyword_location_city_district)
print(data_df_python_keyword)
data_df_python_keyword.insert(9, '城市', data_df_python_keyword_location_city)
data_df_python_keyword.insert(10, '地区', data_df_python_keyword_location_district)
data_df_python_keyword.to_csv("data_df_python_keyword.csv", index=False, encoding='gb18030')
print('-------------------------------------------')
三、Hadoop数据处理（Hive）
首先需要配置好hadoop环境 通过jps查看当前状态
然后进入到Hive分析阶段，****进行词频统计等等操作
这里可以看到Hive表的最终分析后出来的表
hive代码如下：
全国人工智能职位数据 hive建表
create table job_all_info(
workname string,
salary double,
city string,
workyear string,
educate string,
employneed string,
workadvantage string,
companytype string,
companysize string,
workarrange string,
time string
)
热门城市地区人工智能职位数据 hive建表
create table job_all_info_high(
positionName string,
workyear string,
educate string,
skillLables string,
salary double,
cityName string,
regionName string,
workAdvantage string,
companyFinancial string,
workSize string
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde' WITH SERDEPROPERTIES ('separatorChar'=',', 'quoteChar' = '"')
STORED AS TEXTFILE
TBLPROPERTIES ('skip.header.line.count'='1');
load data local inpath '/home/hadoop/hadoop/BossAI_JobInfos.csv' into table job_all_info_high;
select * from job_all_info_high;
alter table job_all_info_high change column salary at double;
Hive部分：利用hive做词频统计 降序排序 分组统计
全国人工智能职位数量分布情况
--------------------------------------------------------------------
CREATE TABLE job_city_info
AS
SELECT city ,count(city) AS quantity FROM job_all_info
group by city order by quantity desc;
--------------------------------------------------------------------
热门城市人工智能职位需求分布情况
--------------------------------------------------------------------
CREATE TABLE job_city_info_high
AS
SELECT cityname ,count(cityname) AS quantity FROM job_all_info_high
group by cityname order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作方向
--------------------------------------------------------------------
CREATE TABLE job_direct_info
AS
SELECT workname ,count(workname) AS quantity FROM job_all_info
order by quantity desc;
--------------------------------------------------------------------
热门城市地区人工智能职位工作方向
--------------------------------------------------------------------
CREATE TABLE job_direct_info_high
AS
SELECT positionName ,count(positionName) AS quantity FROM job_all_info_high
order by quantity desc;
--------------------------------------------------------------------
热门城市地区人工智能公司招聘数量排名
--------------------------------------------------------------------
CREATE TABLE job_company_name
AS
SELECT companyName ,count(companyName) AS quantity FROM job_all_info_high
companyName order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位公司规模
--------------------------------------------------------------------
CREATE TABLE job_company_size_info
AS
SELECT companysize ,count(companysize) AS quantity FROM job_all_info
companysize order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位公司类型
--------------------------------------------------------------------
CREATE TABLE job_company_type_info
b_company_type_info
AS
SELECT companytype ,count(companytype) AS quantity FROM job_all_info
GROUP BY companytype order by quantity desc;
--------------------------------------------------------------------
热门城市人工智能职位公司类型
--------------------------------------------------------------------
CREATE TABLE job_company_type_info_high
AS
SELECT companyfinancial ,count(companyfinancial) AS quantity FROM job_all_info_high
GROUP BY companyfinancial order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作领域
--------------------------------------------------------------------
CREATE TABLE job_company_arrange
AS
SELECT workarrange ,count(workarrange) AS quantity FROM job_all_info
GROUP BY workarrange order by quantity desc;
--------------------------------------------------------------------
热门城市人工智能职位技能需求
--------------------------------------------------------------------
CREATE TABLE job_skill_high_info
AS
SELECT skilllables ,count(skilllables) FROM job_all_info_high
order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作待遇
--------------------------------------------------------------------
CREATE TABLE job_advantage_info
AS
SELECT workadvantage ,count(workadvantage) AS quantity FROM job_all_info
workadvantage order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作学历要求
--------------------------------------------------------------------
CREATE TABLE job_educate_info
AS
SELECT educate ,count(educate) AS quantity FROM job_all_info
GROUP BY educate order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作经验要求
--------------------------------------------------------------------
CREATE TABLE job_workyear_info
AS
SELECT workyear ,count(workyear) AS quantity FROM job_all_info
GROUP BY workyear order by quantity desc;
--------------------------------------------------------------------
全国人工智能职位工作人才缺口
--------------------------------------------------------------------
CREATE TABLE job_employee_info
AS
SELECT employneed ,count(employneed) AS quantity FROM job_all_info
employneed order by quantity desc;
--------------------------------------------------------------------
热门城市人工智能不同工作经验对应薪资
--------------------------------------------------------------------
create TABLE job_workyear_salary
AS
select round(avg(cast(salary as string)),1),workyear from job_all_info_high group by workyear order by workyear asc
--------------------------------------------------------------------
热门城市人工智能不同学历对应薪资
--------------------------------------------------------------------
create TABLE job_educate_salary
AS
select round(avg(salary),1) ,educate from job_all_info_high group by educate order by salary asc
--------------------------------------------------------------------
热门城市人工智能职位最高薪资TOP10
--------------------------------------------------------------------
create TABLE job_Top_salary
AS
select round(avg(salary),1) *0.75 ,positionname from job_all_info_high
order by salary desc limit 10
** 进一步通过Sqoop导入到MySQL中（MySQL需要提前建好表）**
** Sqoop导出过程部分如下**
MySQL部分 将hive中数据利用Sqoop导入MySQL
--------------------------------------------------------------------
create table job_all_info(
workname char(100),
salary double,
city char(100),
workyear char(100),
educate char(100),
employneed char(100),
workadvantage char(100),
companytype char(100),
companysize char(100),
workarrange char(100),
time date
)
create table job_all_info_high(
positionname char(255),
workyear char(255),
educate char(255),
companyname char(255),
skilllable char(255),
salary double,
cityname char(255),
cityregion char(255),
positionAdvantage char(255),
positionType char(255),
companyFinancial char(255)
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_all_info_high --export-dir /user/hive/warehouse/jobdb.db/job_all_info_high
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_city(
cityname char(100),
citycount int
)
create table job_city_high(
cityname char(100),
citycount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_city_info --export-dir /user/hive/warehouse/jobdb.db/job_city_high --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_direct(
workname char(100),
workcount int
)
create table job_direct_high(
workname char(100),
workcount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_direct_info --export-dir /user/hive/warehouse/jobdb.db/job_direct_high --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_workyear(
workyear char(100),
workyearcount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_workyear_info --export-dir /user/hive/warehouse/jobdb.db/job_workyear --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_educate(
educatename char(100),
educatecount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_educate_info --export-dir /user/hive/warehouse/jobdb.db/job_educate --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_employee(
employneedname char(100),
employneedcount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_employneed_info --export-dir /user/hive/warehouse/jobdb.db/job_employee --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_advantage(
workadvantagename char(100),
workadvantagecount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_workadvantage_info --export-dir /user/hive/warehouse/jobdb.db/job_advantage --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_company_type(
companytypename char(100),
companytypecount int
)
create table job_company_type_high(
companytypename char(100),
companytypecount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_companytype_info --export-dir /user/hive/warehouse/jobdb.db/job_company_type --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_company_size(
companysizename char(100),
companysizecount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_companysize_info --export-dir /user/hive/warehouse/jobdb.db/job_company_size --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_company_name(
companyname char(100),
companysize int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_companysize_info --export-dir /user/hive/warehouse/jobdb.db/job_company_name --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
create table job_company_arrange(
workarrangename char(100),
workarrangecount int
)
sqoop-export --connect "jdbc:mysql://cuixinming:3306/jobdb?useUnicode=true&characterEncoding=utf-8" --username root --password root --table job_workarrange_info --export-dir /user/hive/warehouse/jobdb.db/job_company_arrange --input-fields-terminated-by '\001' -m 1
--------------------------------------------------------------------
--------------------------------------------------------------------
MySQL表如下
** 可以通过Navicat访问数据库**
四、数据可视化（echarts）
** 使用MVC模式架构 分层完成可视化大屏**
首先需要定义bean类 与数据库中 表对应
然后定义dao类 获取数据库中对应表的数据（连接数据库部分这里不再赘述）这样一个表的数据就得到了
接着我们需要定义service类将dao中获取的不同表的数据汇总到一起 完成数据聚合 获取数据列表
** 最后的servlet类负责调用service 将获取的数据发送到指定位置**
** 这样数据获取传输部分就完成啦**
全国人工智能数据分析结论（全国人工智能职位）：
1.职位的分布领域情况 计算机软件最多 其次是：互联网、智能硬件、数据服务等
2.人才缺口 职位需求分布情况 目前需求最高的城市是广州、深圳、上海、北京
3.目前受欢迎的职位工作的方向如 最受欢迎的人工智能算法工程师、人工智能训练师、人工智能产品经理
4.招聘公司的融资情况 普遍为民营公司
5.招聘公司的规模 大部分为50-150人左右的公司
6.网上招聘 普遍招聘人数 -绝大部分职位招1人 招3人少一些
7.网上招聘 对工作经验的要求 3-4年比较多、其次是1年经验、在校生
8.网上招聘 对学历要求 本科最多 对硕士 博士要求的较少
9.网上招聘 薪资趋势 普遍在10000元波动 其中8月薪资招聘 平均薪资最高
热门城市人工智能数据分析结论（热门城市人工智能职位）：
1.网上招聘公司招聘发布数量最多 华为、字节跳动、阿里、百度
2.网上招聘对职位的要求 需求量最多：深度学习算法、人工智能、Python、视觉图像
3.人工智能职位 以北京 上海 杭州 西安为边界 区域内人工智能职位比较多
4.薪资最多的人工智能职位为AI数据管理专家120k、视觉生成工程专家75k、AI方向负责人75k
5.薪资对应工作经验 1年以内11k 1-3年15k 3-5年20k 10年以上45k
6.薪资对应学历 本科19.6k 硕士 23.7k 博士32.2k
7.14个热门城市区县 的人工智能职位薪资排名以及总的排名情况
8.目前热门城市AI职位普遍薪资多数在15k-20k左右
五、数据挖掘（PageRank）
技术点：对核心能力和职位进行排序（按照影响力）-PageRank算法
通过PageRank算法我们可以了解到：目前AI职位 核心需求为人工智能技术、深度学习算法、Python等
六、职位薪资预测 (TF-IDF+KNN)
** 处理好的职位数据进行薪资预测**
技术点：
将每个特征占有的比重计算出来 -TFIDF算法
训练数据与模型预测 -KNN回归
流程如下，代码附有注释 欢迎交流~
七、职位查询 (多条件模糊查询)
这里简单的使用模糊查询搜索薪资最高的职位 若有更好的推荐职位的算法欢迎交流~~
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版权归原作者 小崔的金箍棒 所有， 如有侵权，请联系我们删除。