基于pypsark的蛋壳房租价格预测
舟率率 7/14/2025 pythonscalaflask
# 项目概况
# 数据类型
蛋壳房屋价格数据
# 开发环境
centos7
# 软件版本
python3.8.18、hadoop3.2.0、spark3.1.2、mysql5.7.38、scala2.12.18、jdk8
# 开发语言
python
# 开发流程
数据上传(hdfs)->数据清洗(spark)->机器学习(spark)->数据分析(spark)->数据存储(mysql)->后端(flask)->前端(html+js+css)
# 可视化图表
# 操作步骤
# python安装包
pip3 install pandas==2.0.3 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install flask==3.0.0 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install flask-cors==4.0.1 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install pymysql==1.1.0 -i https://mirrors.aliyun.com/pypi/simple/
pip3 install pyecharts==2.0.4 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 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
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# 创建MySQL库
CREATE DATABASE IF NOT EXISTS echarts CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
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# 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
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# 准备目录
mkdir -p /data/jobs/project/
cd /data/jobs/project/
# 上传 "data" 目录下的 "所有" 文件 到 "/data/jobs/project/" 目录
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# 上传文件到hdfs
cd /data/jobs/project/
hdfs dfs -mkdir -p /data/input/
hdfs dfs -rm -r /data/input/*
hdfs dfs -put bj_danke_1.csv /data/input/
hdfs dfs -put bj_danke_2.csv /data/input/
hdfs dfs -put bj_danke_3.csv /data/input/
hdfs dfs -put bj_danke_4.csv /data/input/
hdfs dfs -put bj_danke_5.csv /data/input/
hdfs dfs -put bj_danke_6.csv /data/input/
hdfs dfs -put bj_danke_7.csv /data/input/
hdfs dfs -put bj_danke_8.csv /data/input/
hdfs dfs -ls /data/input/
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# spark数据清洗
cd /data/jobs/project/
# 上传 "数据清洗" 目录下的 "data_clean.py" 文件 到 "/data/jobs/project/" 目录
spark-submit \
--master local[*] \
--jars /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
--driver-class-path /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
data_clean.py /data/input/ /data/output/
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# spark数据分析
cd /data/jobs/project/
# 上传 "数据分析" 目录下的 "data_analysis.py" 文件 到 "/data/jobs/project/" 目录
spark-submit \
--master local[*] \
--jars /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
--driver-class-path /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
data_analysis.py /data/output/
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# 机器学习
cd /data/jobs/project/
# 上传 "机器学习" 目录下的 "ml.py" 文件 到 "/data/jobs/project/" 目录
spark-submit \
--master local[*] \
--jars /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
--driver-class-path /export/software/spark-3.1.2-bin-hadoop3.2/jars/mysql-connector-j-8.0.33.jar \
ml.py /data/output/
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# 启动可视化
mkdir -p /data/jobs/project/myapp/
cd /data/jobs/project/myapp/
# 上传 "可视化" 目录下的 "所有" 文件和文件夹 到 "/data/jobs/project/" 目录
# 先执行 data_extractor.py 创建用户表
python3 data_extractor.py
# windows本地运行: python app.py
python3 app.py pro
# 用户名: admin
# 密码: admin
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