Disaster Response Pipeline Project

Acknowledgements

Many thanks to Figure Eight and Udacity for curating such data sets and offering this hands-on impactful project.

Motivation:

This repository contains my work to adress disaster response by building a web app that can ease emergency worker load to filter disaster messages. The figures below give a taste of the final project.

Project structure

├── app                                                    # Web app files
│   ├── run.py                                             # Scrip to run to execute the app
│   └── templates
│       ├── go.html
│       └── master.html
├── data                                                    # Data directory
│   ├── disaster_categories.csv
│   ├── disaster_messages.csv
│   ├── DisasterResponse.db
│   ├── process_data.py
├── figures                                                 # Figure directory
│   ├── figure_2.png
│   └── figure.png
├── models                                                  
│   ├── classifier.pkl                                      # Serialized model
│   └── train_classifier.py                                 # SCript for training
├── notebooks
│   ├── ETL Pipeline Preparation.ipynb
│   └── ML Pipeline Preparation.ipynb
├── README.md

Dependencies

In this work, I used these libraries along with Python=3.7.4:

• pandas
• matplotlib
• scikit-learn
• nltk

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 clearrun your web app. python run.py

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