Quick disaster response is crucial to help effectively. This project aims to classify messages to ONGs in order to take action in function of the type of need required. A Machine Learning model and a Web app were build up to reach the objective.

1. app

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

2. data

• disaster_categories.csv # data to process
• disaster_messages.csv # data to process
• process_data.py
• DisasteResponse.db # database to save clean data to

3. models

• train_classifier.py
• classifier.pkl # saved model

README.md

1. Run the following commands

   • To run ETL pipeline python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
   • To run ML pipeline python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

2. Go to app directory: cd app

3. Run the web app: python run.py