In this project, we analyze a set of labelled disaster text messages which were collected from actual disaster events by Figure Eight, and build a machine learning pipeline using multiclass logistic regression that is able to categorize emergency messages based on the needs. We also set up a Flask web application that allows an emergency worker to input a new message and get classified results in several categories so that messages can be directed to an appropriate disaster relief agency.

├── app     
│   ├── run.py                           # Flask file that runs app
│   └── templates   
│       ├── go.html                      # Classification result page of web app
│       └── master.html                  # Main page of web app    
├── data                   
│   ├── disaster_categories.csv          # Dataset including all the categories  
│   ├── disaster_messages.csv            # Dataset including all the messages
│   └── process_data.py                  # Build a data cleaning pipeline
├── models
│   └── train_classifier.py              # Build a machine learning pipeline           
└── README.md

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:https://0.0.0.0:3001/