Disaster-Response-Pipeline

Table of Contents

• Motivation
• Files
• Requirements
• Instructions
• Author

Motivation

This project focuses on analyzing disaster data from Figure Eight to build a model that is used to be included in a web-app which allows entering a new message and obtaining the predictions regarding disaster related categories.

Files

- app
| - template
| |- master.html  # main page of web app
| |- go.html  # classification result page of web app
|- run.py  # main Flask file that runs app

- data
|- disaster_categories.csv  # data to process 
|- disaster_messages.csv  # data to process
|- DisasterResponse.db   # database to save clean data to
|- process_data.py

- models
|- train_classifier.py
|- model.pkl  # saved model

- requirements
|- requirements.txt #can be easy to install using pip install requirements

Requirements

You can find all required libraries that used in this project in requirements.txt .

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/model.pkl

2. Run the following command in the app's directory to run your web app. python app/run.py

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

4. The Main page will render

5. Here is example for classifying a message using this app and the result is below

Author

Mahmoud Ahmed

• Github
• Linkedin