Disaster Response Pipeline

Table of Contents

• 1. Installation
• 2. Project Motivation
• 3. Instructions
• 4. Acknowledgments

1. Installation

To run the code successfully, make sure we have these libraries installed in our Python environment:

- pandas
- sqlalchemy
- re
- nltk
- string
- numpy
- joblib
- sklearn

2. Project Motivation

This is the second project from Udacity "Data Science Nanodegree Program". I basically building an interface where you can enter a disaster message, and the system will automatically figure out what type of message it is. This will help in provide the right response and actions during times of disaster. This project is part of the Data Science Nanodegree Program by Udacity, in collaboration with Figure Eight. Dataset are combination tweets and messages from real-life disaster situations.

By using machine learning techniques, we can analyze and categorize the incoming messages quite fast. This means we can respond to disasters more efficiently and quickly. I'll guide you through the whole process of setting up the project. It's cover things like load the dataset, train the machine learning model, and run the web interface. Let's get started!

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. Go to app directory: cd app

3. Run your web app: python run.py

4. Click the PREVIEW button to open the homepage

4. Acknowledgements

• Thanks to Appen (formally Figure 8) for providing the dataset.
• Also Udacity mentors who very helpful with their advice during project work.