pspsn / disaster-response-pipeline

Disaster Response Pipeline Project

Table of Contents

1. Project Motivation
2. Files Descriptions
3. Installation
4. Instructions
5. Screenshots
6. Acknowledgements

Project Motivation & Overview

This code is designed to iniate a web app which an emergency operators could exploit during a disaster (e.g. an earthquake or Tsunami), to classify a disaster text messages into several categories which then can be transmited to the responsible entity

Files Descriptions

The files structure is arranged as below:

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

Installation

All libraries are available in Anaconda distribution of Python. The used libraries are:

• pandas
• re
• sys
• json
• sklearn
• nltk
• sqlalchemy
• pickle
• Flask
• plotly
• sqlite3

The code should run using Python versions 3..

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 your web app. python app/run.py

3. Go to http://127.0.0.1:3001/ to open your localhost

Screenshots

Screenshot 1: App analysis of the data base

Screenshot 2: App word search Page

Acknowledgements

I would like to thank Udacity for this amazing project, and Figure Eight for providing the relevant dataset to train the model.