Disaster Response Pipeline Project

Project Motivation

This projects intends to build an ETL Pipeline with a NLP machine learning model to classify message categories to help in disaster response.

Installation

A basic installation of the Anaconda distribution of Python is sufficient to run the notebook with Python 3.*. The modules imported are:

• numpy
• pandas
• sqlalchemy
• nltk
• sklearn

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

File Descriptions

• app/
  • app/templates/: folder containing the HTML templates of the web app
  • app/run.py: Python script containing the Flask code
• data/
  • disaster_messages.csv: text messages data
  • disaster_categories.csv: categories of the text messages (target of the multi-label classification model)
  • DisasterResponse.db: database file that stores the messages for the model to read
  • process_data.py: Python script containing the ETL pipeline
• models
  • train_classifier.py: Python script containing the model definition and training step

Acknowledgements

Credit goes to Figure Eight for making the data available.