Disaster-Response-Pipelines

Introduction

This repository deals with disaster-related messages using ETL pipelines and NLP tools. It loads in data containing disaster-related messages, cleans the data, trains a machine learning model to classify the data into categories, then presents three data visualizations in a web app. This is a project of Udacity Data Scientist Nanodegree Program.

File structures

• 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 |- DisasterResponse.db # database to save clean data to

• models |- train_classifier.py |- classifier.pkl # saved model, NOT UPLOADED TO GITHUB DUE TO FILE SIZE

• README.md

Instruction

Please follow these steps to run the codes.

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. In another terminal window, run env|grep WORK to get the SPACEDOMAIN and SPACEID. Open a browser window and go to website https://SPACEID-3001.SPACEDOMAIN .