★ FEATURED ARTICLE ✨作者主页IT毕设梦工厂✨个人简介曾从事计算机专业培训教学擅长Java、Python、PHP、.NET、Node.js、GO、微信小程序、安卓Android等项目实战。接项目定制开发、代码讲解、答辩教学、文档编写、降重等。☑文末获取源码☑精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目文章目录一、前言二、开发环境三、系统界面展示四、部分代码设计五、论文参考六、系统视频结语一、前言本系统名为《基于大数据的婚姻幸福指数数据可视化与分析》主要围绕婚姻幸福相关信息展开大数据处理与可视化展示。系统采用Hadoop与Spark作为大数据框架使用HDFS保存原始数据借助Spark SQL、Pandas和NumPy完成数据清洗、缺失值处理、统计汇总和幸福指数相关指标计算后端提供PythonDjango与JavaSpring Boot两个可选版本数据库使用MySQL保存用户信息、婚姻幸福信息和分析结果前端使用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面交互与图表呈现。功能上包含系统首页、大屏可视化、用户、婚姻幸福信息、幸福等级分析、家庭保障分析、乡村数字分析、县域跃迁分析、存款格局分析、自模式洞察分析、个人信息和修改密码。用户可以通过大屏和图表查看不同地区、不同幸福等级下的婚姻幸福指数分布也能从家庭保障、乡村数字、县域跃迁、存款格局和自模式洞察等角度观察数据差异与关联从而对婚姻幸福相关数据形成更直观的认识。系统重点放在大数据统计分析和可视化表达上适合作为计算机专业毕业设计中的大数据应用实践。二、开发环境大数据框架HadoopSpark本次没用Hive支持定制开发语言PythonJava两个版本都支持后端框架DjangoSpring Boot(SpringSpringMVCMybatis)两个版本都支持前端VueElementUIEchartsHTMLCSSJavaScriptjQuery详细技术点Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy数据库MySQL三、系统界面展示基于大数据的婚姻幸福指数数据可视化与分析系统界面展示四、部分代码设计项目实战-代码参考sparkSparkSession.builder.appName(MarriageHappinessBigData).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()defmarriage_info_clean_view(request):raw_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/marriage_db).option(dbtable,marriage_happiness_info).option(user,root).option(password,123456).load()raw_df.createOrReplaceTempView(marriage_happiness_raw)clean_dfspark.sql(SELECT id,user_id,province,county,village,family_income,deposit_amount,family_security_score,marriage_satisfaction,communication_score,child_education_score,elder_care_score,happiness_score FROM marriage_happiness_raw WHERE happiness_score IS NOT NULL)clean_dfclean_df.fillna({family_income:0,deposit_amount:0,family_security_score:0,marriage_satisfaction:0,communication_score:0,child_education_score:0,elder_care_score:0})clean_df.createOrReplaceTempView(marriage_happiness_clean)result_dfspark.sql(SELECT province,county,COUNT(*) AS sample_count,ROUND(AVG(happiness_score),2) AS avg_happiness,ROUND(AVG(family_security_score),2) AS avg_security,ROUND(AVG(deposit_amount),2) AS avg_deposit FROM marriage_happiness_clean GROUP BY province,county ORDER BY avg_happiness DESC)rowsresult_df.collect()data[]forrowinrows:itemrow.asDict()item[happiness_level]高ifitem[avg_happiness]80else中ifitem[avg_happiness]60else低item[deposit_wan]round(item[avg_deposit]/10000,2)data.append(item)returnJsonResponse({code:200,message:婚姻幸福信息处理成功,data:data})defhappiness_level_analysis(request):clean_dfspark.sql(SELECT id,province,county,happiness_score,family_security_score,deposit_amount,family_income FROM marriage_happiness_clean)clean_df.createOrReplaceTempView(happiness_level_source)level_dfspark.sql(SELECT id,province,county,happiness_score,family_security_score,deposit_amount,family_income,CASE WHEN happiness_score90 THEN 非常幸福 WHEN happiness_score75 THEN 比较幸福 WHEN happiness_score60 THEN 一般幸福 ELSE 需要关注 END AS happiness_level FROM happiness_level_source)level_df.createOrReplaceTempView(happiness_level_detail)result_dfspark.sql(SELECT happiness_level,COUNT(*) AS level_count,ROUND(AVG(happiness_score),2) AS avg_score,ROUND(AVG(family_security_score),2) AS avg_security,ROUND(AVG(deposit_amount),2) AS avg_deposit,ROUND(AVG(family_income),2) AS avg_income FROM happiness_level_detail GROUP BY happiness_level ORDER BY avg_score DESC)rowsresult_df.collect()data[]totalsum([row[level_count]forrowinrows])forrowinrows:itemrow.asDict()item[level_ratio]round(item[level_count]/total*100,2)iftotalelse0item[suggestion]保持沟通与保障ifitem[happiness_level]in[非常幸福,比较幸福]else关注家庭保障与存款data.append(item)returnJsonResponse({code:200,message:幸福等级分析成功,data:data})deffamily_security_analysis(request):source_dfspark.sql(SELECT province,county,family_security_score,happiness_score,deposit_amount,family_income FROM marriage_happiness_clean)source_df.createOrReplaceTempView(family_security_source)level_dfspark.sql(SELECT province,county,family_security_score,happiness_score,deposit_amount,family_income,CASE WHEN family_security_score80 THEN 高保障 WHEN family_security_score60 THEN 中等保障 ELSE 低保障 END AS security_level FROM family_security_source)level_df.createOrReplaceTempView(family_security_level)result_dfspark.sql(SELECT province,county,security_level,COUNT(*) AS family_count,ROUND(AVG(happiness_score),2) AS avg_happiness,ROUND(AVG(deposit_amount),2) AS avg_deposit,ROUND(AVG(family_income),2) AS avg_income FROM family_security_level GROUP BY province,county,security_level ORDER BY avg_happiness DESC)rowsresult_df.collect()data[]forrowinrows:itemrow.asDict()item[deposit_wan]round(item[avg_deposit]/10000,2)item[income_wan]round(item[avg_income]/10000,2)item[security_happiness_gap]round(item[avg_happiness]-item[deposit_wan],2)item[advice]保障较好可继续优化存款结构ifitem[security_level]高保障else建议提升家庭保障与储蓄data.append(item)returnJsonResponse({code:200,message:家庭保障分析成功,data:data})五、论文参考计算机毕业设计选题推荐-基于大数据的婚姻幸福指数数据可视化与分析系统-论文参考六、系统视频基于大数据的婚姻幸福指数数据可视化与分析系统-项目视频项目演示视频结语计算机毕业设计选题推荐:基于大数据的婚姻幸福指数数据可视化与分析|毕业设计选题|计算机毕设|选题推荐|毕设指导|项目定制|源码|高质量项目大家可以帮忙点赞、收藏、关注、评论啦源码获取⬇⬇⬇精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目
阅读完成 · 觉得有帮助？
