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ETL_Piplines

Udacity 灾害应对管道项目

一、项目背景

有效的信息传递中一个关键点是信息被传送给正确的接受方,这在灾难相应过程中尤为重要。当发生时,工作人员需要对消息进行分类以送达正确的灾难应急机构。本项目就起到了这么一个作用,可以对消息进行分类。

二、项目结构

1.ETL 管道

process_data.py是一个数据清洗管道,实现了如下功能:

  • 加载 messages 和 categories 数据集
  • 将两个数据集进行合并 (merge)
  • 清洗数据
  • 将其存储到 SQLite 数据库中

2.机器学习管道

train_classifier.py是一个机器学习管道,实现了如下功能:

  • 从 SQLite 数据库中加载数据
  • 将数据集分成训练和测试集
  • 搭建文本处理和机器学习管道
  • 使用 GridSearchCV 对模型进行训练和微调
  • 输出测试集的结果
  • 将最终的模型输出为 pickle 文件

3.Flask 网络应用程序

run.py是一个基于Flask的网络应用程序,用于最终的成果展示

三、其他文件

1.数据源

messages.csv 和 categories.csv 数据集

2.pipline构建过程

ETL Pipeline Preparation-zh.html ML Pipeline Preparation-zh.html

3.模型文件

model.sav

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