Disaster Response Classifier

Summary:

analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages By be creating a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency. In the web app there's an emergency worker that can input a new message and get classification results in several categories. The web app will also display visualizations of the data.

Instructions:

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/

Files:

• app
  | - template
  | |- master.html # main page of web app
  | |- go.html # classification result page of web app
  |- run.py # Flask file that runs app
  

• data
  |- disaster_categories.csv # data to process
  |- disaster_messages.csv # data to process
  |- process_data.py
  |- InsertDatabaseName.db # database to save clean data to
  

• models
  |- train_classifier.py
  |- classifier.pkl # saved model
  

• README.md