1. Motivation

2. Getting Started

   • Packages
   • Structure
   • Instructions
   • Demo

3. Licensing, Authors, Acknowledgements, etc.

During disasters, emergency personel often need to make split second decisions. People are on high alert and share information through social networks at a blazing speed. In this project, I applied my skills to analyze disaster data from Figure Eight, building a model to classify diaster messages, to assist split second decision making. This project incluides a web app for emergany worker to input new massages and help filter out irrelevant messages.

Please find below on how to get started with the project and how to interact with it.

The following packages were used during this project.

• python 3.*
• pandas
• re
• json
• sklearn
• nltk
• sqlite3
• sqlalchemy
• pickle
• flask
• plotly

The filestructure is set up as follows

- README.md: read me file
- \preparation
    - ETL Pipeline Preparation.ipynb: contains ETL pipeline preparation code
    - ML Pipeline Preparation.ipynb: contains ML pipeline preparation code
- \app
    - run.py: flask file to run the app
- \templates
    - master.html: main page of the web application 
    - go.html: result web page
- \data
    - disaster_categories.csv: categories dataset
    - disaster_messages.csv: messages dataset
    - DisasterResponse.db: disaster response database
    - process_data.py: to run the ETL process
- \models
    - train_classifier.py: training the classifier based on the responses in the Database

Once downloaded, this is the way to get started:

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. Go to app directory: cd app

3. Run your web app: python run.py

4. Click the PREVIEW button to open the homepage

A small sample of how the project is currenly looking. A simple interface to input a message. The algorithm classifies the message into different categories.

Feel free to suggest improvments!

In case there are any questions, feel free to reach out to me :) Thanks Udacity for providing the template for the webapp and the preparation for the files.