Disaster Response Pipelines

Table of Contents

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

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.*.

Project Motivation

The goal of the project is to classify the disaster messages into categories. In this project, I analyzed disaster data from Figure Eight to build a model for an API that classifies disaster messages. Through a web app, the user can input a new message and get classification results in several categories. The web app also display visualizations of the data.

Project Descriptions

The project has three componants which are:

1. ETL Pipeline: process_data.py file contain the script to create ETL pipline which:

• Loads the messages and categories datasets
• Merges the two datasets
• Cleans the data
• Stores it in a SQLite database

2. ML Pipeline: train_classifier.py file contain the script to create ML pipline which:

• Loads data from the SQLite database
• Splits the dataset into training and test sets
• Builds a text processing and machine learning pipeline
• Trains and tunes a model using GridSearchCV
• Outputs results on the test set
• Exports the final model as a pickle file

3. Flask Web App: the web app enables the user to enter a disaster message, and then view the categories of the message.

The web app also contains some visualizations that describe the data.

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 web page
	- \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

Instructions

To execute the app follow the 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 run.py

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