Disaster Response Pipeline Project

A Machine Learning Pipeline to categorize messages sent during disaster events, so that they can be forwared to the appropriate disaster relief agency.

It includes a web app for data visualization, where an emergency worker can input a new message and get classification results in several categories.

The data set, provided by Figure Eight, contains real messages that were sent during disasters.

Project Components:

The projects consists of three main components:

ETL Pipeline: process_data.py

Data Preprocessing using an ETL Pipeline.

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

Arguments:

• messages_filepath: path to the csv file containing messages (e.g. disaster_messages.csv)
• categories_filepath: path to the csv file containing categories (e.g. disaster_categories.csv)
• database_filename: path to SQLite destination database (e.g. disaster_response_db.db)

ML Pipeline: train_classifier.py

Trains a multi-label classifier for text messages using a ML Pipeline

• 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

Arguments:

• database_filepath: path to SQLite destination database (e.g. disaster_response_db.db)
• model_filepath: path to pickle file name where ML model needs to be saved (e.g. classifier.pkl)

Flask Web App

Search Bar:

Overview of training dataset:

Example of a message classification:

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/