Disaster Response Pipeline Project

This project analyzes disaster data from Figure Eight to build a model for an API that classifies disaster messages. The data set used in this project contains real messages that were sent during disaster events. A machine learning pipeline is created to categorize these events so that the messages can be sent to the appropriate disaster relief agency. The project includes a web app where an emergency worker can input a new message and get classification results in several categories. The web app displays visualizations of the data as well.

Table of Contents

• Installation
• File Description
• Instructions to Run the Project
• Results
• Acknowledgement

Installation

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.x.

See requirements.txt.

File Description

This repo contains 3 folders which includes work related to data transformation, data modeling and app development. Here's the file structure of the project:

.
├── app
├   ├── templates
├   ├   ├── go.html               # classification result page of web app
├   ├   ├── master.html           # main page of web app
├   ├── run.py                    # Flask file that runs app
├── data 
├   ├── disaster_categories.csv   # data to process
├   ├── disaster_messages.csv     # data to process
├   ├── DisasterResponse.db       # saved database
├   ├── process_data.py           # script to run ETL pipeline
├── model
├   ├── train_classifier.py       # script to run ML pipeline
├   ├── classifier.pkl            # saved model
├── Requirements.txt
└── README.md

Instructions to Run The Project

1. Run the following commands in the project's root directory to set up 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 web app. python run.py

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

Results

The main findings, which is the web app can be found by running python scripts.

Acknowledgement

Must give credit to Figure Eight for the data.