作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目目录拉勾网招聘数据可视化分析系统介绍拉勾网招聘数据可视化分析系统演示视频拉勾网招聘数据可视化分析系统演示图片拉勾网招聘数据可视化分析系统代码展示拉勾网招聘数据可视化分析系统文档展示拉勾网招聘数据可视化分析系统介绍本系统名为《基于大数据的拉勾网招聘数据可视化分析》是一个面向计算机专业毕业设计的招聘数据处理、分析与可视化平台。系统以招聘信息为核心数据使用Hadoop的HDFS做数据存储借助Spark和Spark SQL完成数据清洗、分组统计、指标计算和简单分群配合Pandas、NumPy做辅助处理分析结果存入MySQL。后端提供PythonDjango和JavaSpring Boot两个版本前端采用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面展示与图表交互。功能上包含系统首页、数据大屏、用户管理、招聘信息管理、地域分布分析、薪资水平分析、岗位结构分析、应聘门槛分析、企业特征分析、福利标签分析、岗位分群分析、个人信息和修改密码等模块。用户可以在首页查看整体数据概况在数据大屏中观察招聘信息的核心指标变化在招聘信息模块维护和查询原始记录再通过地域分布、薪资水平、岗位结构、应聘门槛、企业特征、福利标签和岗位分群等页面从不同角度了解招聘数据的分布与差异。系统不追求复杂算法更强调把大数据处理流程、后端接口、数据库存储和前端可视化串成一条完整链路适合作为本科阶段综合实践项目也方便后续按需扩展分析维度。拉勾网招聘数据可视化分析系统演示视频点击观看项目演示视频拉勾网招聘数据可视化分析系统演示图片拉勾网招聘数据可视化分析系统代码展示sparkSparkSession.builder.appName(RecruitBigDataAnalysis).master(local[*]).getOrCreate()defregion_distribution_analysis():dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/recruit_db).option(dbtable,recruit_info).option(user,root).option(password,123456).load()df.createOrReplaceTempView(recruit_info)region_dfspark.sql( select city, count(*) as job_count, round(avg(salary_min), 2) as avg_salary_min, round(avg(salary_max), 2) as avg_salary_max, sum(case when education like %本科% then 1 else 0 end) as bachelor_count, sum(case when experience like %应届% then 1 else 0 end) as fresh_count, collect_set(company_type) as company_types, collect_set(welfare_tags) as welfare_list from recruit_info where city is not null and city ! group by city order by job_count desc )region_pdregion_df.toPandas()region_pd[salary_avg](region_pd[avg_salary_min]region_pd[avg_salary_max])/2region_pd[job_ratio]region_pd[job_count]/region_pd[job_count].sum()region_pd[bachelor_ratio]region_pd[bachelor_count]/region_pd[job_count]region_pd[fresh_ratio]region_pd[fresh_count]/region_pd[job_count]region_pd[main_company]region_pd[company_types].apply(lambdax:list(x)[:3]ifxelse[])region_pdregion_pd.sort_values(job_ratio,ascendingFalse)resultregion_pd[[city,job_count,salary_avg,job_ratio,bachelor_ratio,fresh_ratio,main_company]].to_dict(orientrecords)returnresultdefsalary_level_analysis():dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/recruit_db).option(dbtable,recruit_info).option(user,root).option(password,123456).load()df.createOrReplaceTempView(recruit_info)salary_dfspark.sql( select case when salary_min 5000 then 5k以下 when salary_min 5000 and salary_min 10000 then 5k-10k when salary_min 10000 and salary_min 15000 then 10k-15k when salary_min 15000 and salary_min 20000 then 15k-20k else 20k以上 end as salary_level, count(*) as job_count, round(avg(salary_min), 2) as avg_min, round(avg(salary_max), 2) as avg_max, round(avg((salary_min salary_max) / 2), 2) as avg_salary, collect_set(city) as city_list, collect_set(education) as education_list from recruit_info where salary_min is not null and salary_max is not null group by salary_level order by job_count desc )salary_pdsalary_df.toPandas()salary_pd[salary_mid](salary_pd[avg_min]salary_pd[avg_max])/2salary_pd[job_ratio]salary_pd[job_count]/salary_pd[job_count].sum()salary_pd[city_count]salary_pd[city_list].apply(lambdax:len(set(x))ifxelse0)salary_pd[education_count]salary_pd[education_list].apply(lambdax:len(set(x))ifxelse0)salary_pd[top_city]salary_pd[city_list].apply(lambdax:list(x)[:5]ifxelse[])salary_pdsalary_pd.sort_values(job_ratio,ascendingFalse)returnsalary_pd[[salary_level,job_count,salary_mid,job_ratio,city_count,education_count,top_city]].to_dict(orientrecords)defjob_group_analysis():dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/recruit_db).option(dbtable,recruit_info).option(user,root).option(password,123456).load()df.createOrReplaceTempView(recruit_info)base_dfspark.sql( select job_name, company_type, education, experience, salary_min, salary_max, city, welfare_tags, (salary_min salary_max) / 2 as salary_avg, case when education like %本科% then 1 else 0 end as need_bachelor, case when experience like %应届% then 1 else 0 end as fresh_ok, size(split(welfare_tags, ,)) as welfare_count from recruit_info where job_name is not null and salary_min is not null and salary_max is not null )base_df.createOrReplaceTempView(job_base)group_dfspark.sql( select case when salary_avg 18000 and need_bachelor 1 then 高薪高门槛 when salary_avg 18000 and need_bachelor 0 then 高薪低门槛 when salary_avg 18000 and need_bachelor 1 then 普通高门槛 else 普通低门槛 end as job_group, count(*) as job_count, round(avg(salary_avg), 2) as avg_salary, round(avg(welfare_count), 2) as avg_welfare, sum(fresh_ok) as fresh_count, collect_set(company_type) as company_types, collect_set(city) as city_list from job_base group by job_group order by job_count desc )group_pdgroup_df.toPandas()group_pd[group_ratio]group_pd[job_count]/group_pd[job_count].sum()group_pd[fresh_ratio]group_pd[fresh_count]/group_pd[job_count]group_pd[main_company]group_pd[company_types].apply(lambdax:list(x)[:3]ifxelse[])group_pd[main_city]group_pd[city_list].apply(lambdax:list(x)[:5]ifxelse[])returngroup_pd[[job_group,job_count,avg_salary,avg_welfare,group_ratio,fresh_ratio,main_company,main_city]].to_dict(orientrecords)拉勾网招聘数据可视化分析系统文档展示作者计算机毕业设计小途个人简介曾长期从事计算机专业培训教学本人也热爱上课教学语言擅长Java、微信小程序、Python、Golang、安卓Android等开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法也喜欢交流技术大家有技术代码这一块的问题可以问我想说的话感谢大家的关注与支持网站实战项目安卓/小程序实战项目大数据实战项目深度学习实战项目