Disaster Response Pipeline Project

This project is to classify disaster response messages through machine learning.

Content

• Data

  • process_data.py: reads in the data from CSV files, cleans and stores it in a SQL database. Basic usage is: python process_data.py MESSAGES_DATA CATEGORIES_DATA NAME_FOR_DATABASE
  • disaster_categories.csv and disaster_messages.csv (dataset)
  • DisasterResponse.db: created database from transformed and cleaned data.

• Models

  • train_classifier.py: includes the code necessary to load data, transform it using natural language processing, run a machine learning model using GridSearchCV and train it. Basic usage is python train_classifier.py DATABASE_DIRECTORY SAVENAME_FOR_MODEL

• App

  • run.py: Flask app and the user interface used to predict results and display them.
  • templates: folder containing the html templates Basic usage is python run.py

Instructions:

1. Run the following commands in theprocess_data project's root directory to set up your database and model.

   • To run ETL pipeline that cleans data and stores in database

     python 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/

Screenshots

This is the frontpage:

By inputting a word, you can check its category:

About

This project was prepared as part of the Udacity Data Scientist nanodegree programme. The data was provided by Figure Eight.