Disaster Response Pipeline

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/

4. I've learned and built on your data engineering skills to expand my opportunities and potential as a data scientist. In this project, I have applied these skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

5. I find a data set containing real messages that were sent during disaster events. You will be creating a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.

6. My project will include a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data. This project will show off your software skills, including your ability to create basic data pipelines and write clean, organized code!