GithubHelp home page GithubHelp logo

disasterresponse's Introduction

DisasterResponse Project

  • This is a project analyzing message data for disaster response.

Above All

  • Before you start read this page, please check your system env first.
  • This project developed in Ubuntu 18.04 LTS 64 bit with Python 3.6.6 (ATTENTION: NOT Anaconda Env.!).
  • After develop this project, I also test the pipeline scripts in Windows 10 64bit with Python 3.7 64bit (ATTENTION: NOT Anaconda Env.!).
  • Please don't use the Anaconda Env, and I suggest you to use Ubuntu or any other Linux to run it.

Project Infomation

  • This project develop under Python 3.6.6. Let's have a look of the directory tree:
DisasterResponse/                   --> project directory
├── data                            --> dataset directory(only for notebook)
│   ├── categories.csv
│   └── messages.csv
├── ETL Pipeline Preparation.html   --> html export from notebook
├── ETL Pipeline Preparation.ipynb  --> ETL pipeline notebook
├── LICENSE                         --> license file
├── ML Pipeline Preparation.html    --> html export from notebook
├── ML Pipeline Preparation.ipynb   --> ML pipeline notebook
├── pipeline                        --> pipeline project directory
│   ├── app                         --> the flask app directory
│   │   ├── run.py                  --> flask main run file
│   │   └── templates               --> flask templates directory
│   │       ├── go.html
│   │       └── master.html
│   ├── data                        --> dataset directory for pipline
│   │   ├── disaster_categories.csv
│   │   ├── disaster_messages.csv
│   │   └── process_data.py         --> process_data for ETL pipeline
│   ├── models                      --> models directory
│   │   └── train_classifier.py     --> train_classifier for ML pipeline
│   └── README.md                   --> pipeline README file
└── README.md                       --> main README file
  • The requirement pkgs:
nltk                    3.3
pickleshare             0.7.4
pandas                  0.23.2
numpy                   1.14.5
SQLAlchemy              1.2.12
sklearn                 0.0
scipy                   1.1.0

Run this Project

  1. cd to the pipeline directory
  2. python3 data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
  3. python3 models/train_classifier.py data/DisasterResponse.db models/classifier.pkl
  4. cd to the pipeline/app directory
  5. python3 run.py
  6. go to http://0.0.0.0:3001/

Something Else

  • If you have any trouble when you use the pipeline, at first you need to read the README.md in pipeline/ directory.
  • Maybe you can Email me: [email protected]

disasterresponse's People

Contributors

kylechenoo avatar

Stargazers

Baymax  avatar  avatar WangYue avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.