基于spark电商用户流失预警系统
舟率率 5/13/2026 pythonspringbootjavavue
# 项目概况
# 数据类型
# 开发环境
centos7
# 软件版本
python3.8.18、hadoop3.2.0、spark3.1.2、mysql5.7.38、jdk8
# 开发语言
python、Scala、Java
# 开发流程
数据上传(hdfs)->数据分析(spark)->机器学习(spark)->数据存储(mysql)->后端(springboot)->前端(vue)
# 可视化图表
# 操作步骤
# python安装包
pip3 install numpy==1.24.4 -i https://pypi.tuna.tsinghua.edu.cn/simple/
1
2
3
2
3
# 启动MySQL
# 查看mysql是否启动 启动命令: systemctl start mysqld.service
systemctl status mysqld.service
# 进入mysql终端
# MySQL的用户名:root 密码:123456
# MySQL的用户名:root 密码:123456
# MySQL的用户名:root 密码:123456
mysql -uroot -p123456
1
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
# 启动Hadoop
# 离开安全模式: hdfs dfsadmin -safemode leave
# 启动hadoop
bash /export/software/hadoop-3.2.0/sbin/start-hadoop.sh
1
2
3
4
5
2
3
4
5
# 准备目录
mkdir -p /data/jobs/project/
cd /data/jobs/project/
# 解压 "data" 目录下的 "data.7z" 到当前目录下
# 上传 "project-spark-e-commerce-user-churn-warning-system" 整个文件夹 到 "/data/jobs/project/" 目录
1
2
3
4
5
6
7
2
3
4
5
6
7
# 上传文件到hdfs
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/
hdfs dfs -mkdir -p /data/input/
hdfs dfs -rm -r /data/input/*
hdfs dfs -put data/customer_churn_business_dataset.csv /data/input/
hdfs dfs -ls /data/input/
1
2
3
4
5
6
7
8
2
3
4
5
6
7
8
# 程序打包
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/spark-job/
# 对 "spark-job" 目录下的项目 "spark-job" 进行打包
# 打包命令
mvn clean package -DskipTests
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/churn-warning-system-backend/
# 对 "spark-job" 目录下的项目 "spark-job" 进行打包
# 打包命令
mvn clean package -DskipTests
1
2
3
4
5
6
7
8
9
10
11
12
13
2
3
4
5
6
7
8
9
10
11
12
13
# 创建MySQL表
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/mysql/
# 请确认mysql服务已经启动了
# 快速执行.sql文件内的sql语句
mysql -uroot -p123456 < mysql.sql
1
2
3
4
5
6
7
2
3
4
5
6
7
# spark数据分析
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/spark-job/
spark-submit \
--master local[*] \
--class org.example.ChurnAnalyzer \
target/spark-job.jar /data/input/
1
2
3
4
5
6
7
8
2
3
4
5
6
7
8
# spark流失预测
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/
spark-submit --master local[*] ml.py /data/input/
1
2
3
4
5
2
3
4
5
# 启动后端
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/churn-warning-system-backend/
java -jar target/churn-warning-system-backend-1.0.0.jar
1
2
3
4
5
2
3
4
5
# 启动前端
cd /data/jobs/project/project-spark-e-commerce-user-churn-warning-system/churn-warning-system-frontend/
# 安装node
npm install --registry=https://registry.npmmirror.com
chmod -R 755 node_modules/.bin
npm run dev
# http://localhost:8080
# 登录用户: admin@example.com
# 登录密码: 123456
1
2
3
4
5
6
7
8
9
10
11
2
3
4
5
6
7
8
9
10
11