Disaster Response Pipeline Project

This project is a part of the Data Science Nanodegree Program by Udacity.

Introduction
File Descriptions
Instructions
Results
Acknowledgements

1. Introduction

The aim of this project is to build a machine learning model to analyze disaster data from Figure Eight that classifies disaster messages, and show the result in an interactive webpage.

2. File Descriptions

- 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  # script to process data
|- DiastersDB.db   # database to save clean data to

- models
|- train_classifier.py   # script to train model
|- classifier.pkl  # saved model 

- results
|- screenshot-1    # distribution of message categories
|- screenshot-2    # top 10 distribution of message categories in percentage
|- screenshot-3    # distribution of message genres
|- screenshot-4    # an example of disaster response webpage

- README.md

3. Instructions

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/DisastersDB.db
- To run ML pipeline that trains classifier and saves python models/train_classifier.py data/DisastersDB.db models/classifier.pkl
Run the following command in the app's directory to run your web app. python run.py
Go to http://0.0.0.0:3001/

4. Results

Screenshots from the web application.

5. Acknowledgmements

I would like to thank Udacity and Figure Eight for their support in this project.