Disaster Response Pipeline Project

A simple web app which visualize and classifies disaster response messages using machine learning algorithms applied on real messages datasets.

Table of Content

1. Instruction
2. Project Motivation
3. File Descriptions
4. Licensing, Authors, and Acknowledgements

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 the web app. python run.py

3. Go to http://0.0.0.0:3001/

Project Motivation

The goal here is building a model based on a data containing thousands of messages, provided by Figure Eight, that were sent during natural disasters. These messages were sent either via social media or directly to disaster response organizations. I have built an ETL pipeline that processes message and category data from CSV files, and load them into a SQLite database, which the machine learning pipeline will then read from to create and save a multi-output supervised learning model. The result will be demonstrated as a visualization and an interactive classification of messages through a web app.

File Descriptions

There are 3 folders: . app containing run.py and html templates . data containing csv files and process_data.py . models containing train_classifier.py

Licensing, Authors, Acknowledgements

Must give credit to Figure Eight for the data and Udacity for giving the necessary trainings. Otherwise, feel free to use the app.