Disaster Response Pipeline Project

Project Overview

In this project we analyze disaster data to build a machine learning model using scikit-learn for an API that classifies disaster messages. The data set is made up of real messages that were sent during disaster events. We create a machine learning pipeline to categorize these events so that the messages can be sent to an appropriate disaster relief agency.

This projects include a web app where users can input new messages and get classification results in several categories. The web app also display some visualizations of the data.

Relevant Files:

• process_data ETL Pipeline that:

  • Loads the messages and categories datasets
  • Merges the two datasets
  • Cleans the data
  • Stores it in a SQLite database

• train_classifier ML Pipeline that:

  • Loads data from the SQLite database
  • Splits the dataset into training and test sets
  • Builds a text processing and machine learning pipeline
  • Trains and tunes a model using GridSearchCV
  • Outputs results on the test set
  • Exports the final model as a pickle file

• run: Start the server for the Flask Web App

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 app/run.py

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