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Disaster Response Pipeline Project

Background:

This project is a requirement for project 3 of the Udacity Data Science Nanodegree. The idea is to build a machine learning pipeline to evaluate plaintext in the context of disaster response. Training data is provided as classified plaintext in csv format. The outcome is a flask app which categorises plaintext messages into 36 applicable disaster response classes.

Prerequisites

The following needs to be installed:

  • python 3.7
  • json
  • plotly
  • pandas
  • nltk
  • flask
  • plotly
  • sklearn
  • sqlalchemy
  • sys
  • re
  • pickle
  • numpy

Data

  • ./data/disaster_messages.csv gives messages in plaintext.
  • ./data/disaster_categories.csv gives the 36 target classes. These csv files are imported and joined using an inner merge.

Running the model

  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

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