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Disaster Response Pipeline Project

Web App Home Page

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.