Disaster Response Pipeline Project

Table of Contents

1. Project Overview
2. File Structure - ETL Pipeline - data - ML Pipeline - models - Flask Web App - app
3. Running Instructions
4. Acknowledgement

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Project Overview

This project processes messages data from Figure Eight and classifies into 36 categories. It includes a web application that allows an emergency worker to input a message and get categories results and presents visualization charts of data.

File Structure

ETL Pipeline - data

File '/data/process_data.py' stores the ETL pipeline that loads, merges and clean 'categories' and 'messages' and stores in a SQLite database.

ML Pipeline - models

File '/models/train_classifier.py' stores text processing and machine learning pipeline that:

• loads data from SQLite database
• split train and test datasets
• train and tune models using Random Forest Classifier and GridSearchCV
• predict on the test dataset
• save model in a pickle file

Flask Web App - app

Below is a screen-shot of the web app. It allows an emergency worker to input a message and get the classification results. Below the search bar, it presents several visualization charts of dataset.

Running Instructions

1. Run the following command in the project's root directory: python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db

2. Run the following command in the project's root directory: python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

3. Run the Flask web app in the app's directory 'python run.py'/; Check the web app from http://0.0.0.0:3001/

Acknowledgement

Thank Figure Eight for providing data and Udacity for the instructions and advice.