Disaster Response Pipeline Project

Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. The data set contain real messages that were sent during disaster events. Code create a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency. Project also include a web app where an emergency worker can input a new message and get classification results in several categories. The web app also display visualizations of the training data.

Table of Contents

1. Instructions
2. File Descriptions
3. Licensing, Authors, Acknowledgements

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 it into a .pkl file

   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/

File Descriptions

• app
  • templates/go.html: Web page that handles user query and displays model results
  • templates/master.html: Web page with plotly graphs
  • run.py: Run web application
• data
  • disaster_categories.csv: Data for categories which are the target variables
  • disaster_messages.csv: Data for messages which features are built from
  • process_data.py: Run ETL pipeline that cleans data and stores in database
• models
  • train_classifier.py: Run ML pipeline that trains classifier and save model into .pkl file

Licensing, Authors, Acknowledgements

Big thank you to Udacity for providing the template code for this project. Also want to thank Figure Eight for providing the data.