Disaster Response Pipeline Project

Project Motivation

This project analyzes the disaster data from Figure Eight to build a model for an API that classifies disaster messages. This project is helpful for an emergency worker who can access it via web app included as part of the project.

Project Structure

  .
  ├─ app
  │   ├─ run.py                           | Flask file that runs app
  │   └─ templates
  │       ├─ go.html                      | Classification result page
  │       └─ master.html                  | Master page of web app
  ├─ data
  │   ├─ disaster_categories.csv          | Disaster Categories Dataset
  │   ├─ disaster_messages.csv            | Disaster Messages Dataset
  |   ├─ DisasterResponse.db              | Disaster Response Database to be used by the Web-App
  │   └─ process_data.py                  | Data cleaning
  ├─ models
  │   └─ train_classifier.py              | Train ML model
  │   └─ classifier.pkl                   | Python Pickle File
  └─ README.md

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://0.0.0.0:4111/