rahul263-stack/disaster-response-pipeline

Disaster Response Pipeline Project

1. Project Overview In this project, I'll apply data engineering to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

data directory contains a data set which are real messages that were sent during disaster events. I will be creating a machine learning pipeline to categorize these events so that appropriate disaster relief agency can be reached out for help.

This project will include 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.

Here are a few screenshots of the web app. File Description

.
├── app     
│   ├── run.py                           # Flask file that runs app
│   └── templates   
│       ├── go.html                      # Classification result page of web app
│       └── master.html                  # Main page of web app    
├── data                   
│   ├── disaster_categories.csv          # Dataset including all the categories  
│   ├── disaster_messages.csv            # Dataset including all the messages
│   └── process_data.py                  # Data cleaning
├── models
│   └── train_classifier.py              # Train ML model           
└── README.md

Instructions: (Run run.py directly if DisasterResponse.db and claasifier.pkl already exist.)

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:" http://127.0.0.1:5000/"