/udacity_disaster_response_pipeline

disaster_response_pipeline_project

Primary LanguagePython

Disaster Response Pipeline Project

Project Introductions

This project is originally based on "Disaster Response Pipeline" from Udacity. The goal is to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

There are mainly three parts in the project:

  1. Build the ETL pipeline (in the data folder)
  2. Build the Machine Learning Pipeline (in the models folder)
  3. Build the Flask Web Application (in the app folder)

Instructions:

  1. Run the following commands in the project's root directory to set up your database and model.

    • To run ETL pipeline that cleans data and stores in database python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
    • To run ML pipeline that trains classifier and saves python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

  2. Run the following command in the app's directory to run your web app. python run.py

  3. Go to http://0.0.0.0:3001/

File Descriptions

  • app
    | - template
    | |- master.html # main page of web app
    | |- go.html # classification result page of web app
    |- run.py # Flask file that runs app

  • data
    |- disaster_categories.csv # data to process
    |- disaster_messages.csv # data to process
    |- process_data.py # The main ETL process is here
    |- DisasterResponse.db # database to save clean data to

  • models
    |- train_classifier.py # The main ML process is here
    |- classifier.pkl # saved model

  • README.md