Disaster Response Pipeline Project

In this project, I applied data engineering skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

In the Project Workspace, you'll find a data set containing real messages that were sent during disaster events. I created a machine learning pipeline to categorize these events so that we can send the messages to an appropriate disaster relief agency.

My project includes a web app where an emergency worker can input a new message and get classification results in several categories:

The web app also displays two visualizations of the data:

The structure of the project:

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/

Dependencies:

numpy, pandas, matplotlib, json, plotly, nltk, flask, sklearn, sqlalchemy, sys, re, pickle

This project also requires Python 3.x along with the above libraries installed as a pre-requisite