Disaster Response Pipeline Project

Project Summary

In the Project, I worked on a data set containing real messages that were sent during disaster events. I created a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.

This project also includes a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data.

Repo directory structure

├── README.md
├── models
|   ├── train_classifier.py
|   └── classifier.pkl          # saved model 
├── data
|   ├── process_data.py
|   ├── disaster_categories.csv # data to process 
|   ├── disaster_messages.csv   # data to process
|   └── DisasterResponse.db     # database to save clean data to
├── app
    ├── run.py
    └── templates
        ├── go.html             # classification result page of web app
        └── master.html         # main page of web app

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/ or http://localhost:3001/