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~~

版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.csdn.net/cxm259/article/details/118961604