Disaster Response Pipelines

Table of Contents

1. Project Motivation
2. Project Description
3. Files Description
4. Instructions
5. Dependencies
6. Acknowledgements
7. Web App Screenshots

Project Motivation

The goal of the project is to analyze disaster data and build a Natural Language Processing (NLP) model for an API that classifies disaster messages. This dataset is provided by Figure Eight which contains pre-labelled tweet and messages from real-life disaster events. Through a web app developed with this project, an emergency worker can input a new message and get a prediction for the response categories the message is likely to belong, reducing the potential reaction time of the responding organizations.

Project Descriptions

The project has three components:

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 Description

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
	- \Data
		- disaster_categories.csv: categories dataset
		- disaster_messages.csv: messages dataset
		- DisasterResponse.db: disaster response database
		- process_data.py: ETL process

	- \app
		- run.py: flask file to run the app

	- \templates
		- master.html: main page of the web application 
		- go.html: result web page

	- \images
		- Screenshot_app_mainpage: app home page
		- Screenshot_app_message_classifier_page: app message classifier page
	- \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/

Dependencies

• Python 3.*
• Machine Learning Libraries: NumPy, SciPy, Pandas, Scikit-Learn
• Natural Language Process Libraries: NLTK
• SQLlite Database Libraries: SQLalchemy
• Model Loading and Saving Library: Pickle
• Web App and Data Visualization: Flask, Plotly

Acknowledgements

-Figure Eight for providing the relevant dataset to train the model

Web App Screenshots

1. Homepage

2. Message classifier page