Disaster Response

• Disaster Response
  • 1. Background
  • 2. File Descriptions
  • 3. Instructions
  • 4. Python version and libraries
  • 5. Screenshots of web application

1. Background

This project uses natural language processing (NLP) and machine learning to classify social media messages for disaster events.

Objectives: (1) build a command line application that creates messages classifier based on input dataset; and (2) build a web application that visualizes the dataset and predicts categories of a new input message.

Dataset: The dataset of social media messages for disaster events and 36 categories was given by Udacity Nanodegree program in partner with Figure Eight.

2. File Descriptions

• app

  • template
    • master.html: homepage of web app
    • go.html: classification result page of web app
  • run.py: Flask file that runs web app
  • utils.py: customized functions and transformers that support run.py

• data

  • disaster_categories.csv: message categories data to process
  • disaster_messages.csv: messages data to process
  • process_data.py: ETL pipeline to clean data and store data into SQLite database
  • ETL Pipeline Preparation.ipynb: ETL pipeline prepared in a notebook
  • DisasterResponse.db: database of cleaned data

• models

  • train_classifier.py: machine learning pipeline to train a classification model and store the trained model into a pickle file
  • ML Pipeline Preparation.ipynb: machine learning pipeline prepared in a notebook
  • utils.py: customized functions and transformers that support train_classifier.py
  • classifier.pkl: pickle file of trained model

• README.md

3. Instructions

• Command line application

  Run the following commands in the project's root directory to set up the 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 machine learning pipeline that trains classifier and saves
    python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl.

• Web application

  Run the following command in the app directory to launch the web app: python run.py.
  Go to http://0.0.0.0:3001/.

4. Python version and libraries

The code was developed using the Anaconda distribution of Python version 3.6. The following dependencies were used.

pandas
sqlalchemy
nltk
sklearn
plotly
spacy (en_core_web_sm and en_core_web_lg)
flask

5. Screenshots of web application

• Homepage

  

• Classification result page