/Disaster-Response_Pipeline

Disaster Response Pipeline Project from Udacity's Data Science Nanodegree.

Primary LanguageJupyter Notebook

Disaster Response Pipelines

1. [Description]

2. [Project Motivation]

3. [File Descriptions]

4. [Installation]

1. Description


Disaster Response Pipeline Project from Udacity's Data Science Nanodegree.

We will make an AI pipeline to arrange calamity occasions with the goal that we can send the messages to a proper catastrophe alleviation office. We will prepare on informational index containing genuine messages that were sent during catastrophe occasions from Figure Eight.

2. Project Motivation

Make an AI pipeline to classify these occasions with the goal that clients can send the messages to a suitable calamity alleviation organization.

3. File Descriptions

  • data/disaster_messages.csv data/disaster_categories.csv : original data
  • data/process_data.py: to run ETL pipeline that cleans data and stores in database
  • data/DisasterResponse.db: database that stores cleaned data
  • models/train_classifier.py: to run ML pipeline that trains classifier and saves
  • models/classifier.pkl: a pickle file which saves model
  • data/: a Flask framework for presenting data

4. Instructions

1. Run the accompanying commands in the venture'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`

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

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

Code structure

  • 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
    • InsertDatabaseName.db # database to save clean data to

  • models

    • train_classifier.py
    • classifier.pkl # saved model

  • README.md