Disaster Response Pipeline Project

Checkout the deployed version of this project on Heroku (ctrl/cmd + Click)

Table of Contents

1. Introduction
2. Data
3. Project Components
4. Instructions to Run
5. Screenshots
6. API
7. Coming Soon

Introduction

Goal of this project is to build a ML pipeline that can classify disterss messages originating from social media during a natural disaster. In addition, emergencey care workers should have access to the classifcation ML model via a web frontend. Interacting with the frontend, a care worker can input the message and get the message classified into one of the 36 categories

Data and files

In this project, disaster data from Figure Eight is used.

data folder

• process_data.py: This script is used for performing ETL on the data and storing the transformed data into a SQLite database.
• disaster_categories.csv, disaster_messages.csv, DisasterResponse.db are all data files

models folder

• train_classifier.py: This is the script used to build, train, evaluate, and export the model to pickle file that can be used as basis for model in production.

app folder

• run.py: This is the script used for the web app, starting the server and displaying visualizations.
• go.html, master.html are templates for frontend of webapp.

Project Components

There are three components in this project.

1. ETL Pipeline

Python script, process_data.py, contains data cleaning pipeline that:

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

2. ML Pipeline

Python script, train_classifier.py, contains machine learning 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

3. Flask Web App

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.

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" directory to run your web app. python run.py

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

Webapp UI and example

Home page- Display Visualizations

Example message classification

Using API

Alternative to the webapp, you can classify message using API. To do this you need to "POST" request your message in JSON format.

API request using curl

curl --location --request POST 'https://disastermessage-classification.herokuapp.com/classify' \
--header 'Content-Type: application/json' \
--data-raw '{
    "query":"<YOUR_MESSAGE>"
}'

And the response would be something as shown below:

{
  "classification_result": [
    <YOUR_PREDICTED_OUTPUT>
  ], 
  "query": "<YOUR_MESSAGE>", 
  "success": true
}

API request and response using Postman

Example Request and Response using Postman

Coming soon

Role-based access control (RBAC) for API using Auth0