Disaster Response Pipeline Project

Overview

This project contains a web app that asks for a message from a potential user who is in danger during a disaster and the app categorizes that message into a particular category such as aid related, weather related, fire or many more.

Description of files

• App: Contains javascript files and app.py file which implements Flask & Plotly to create the web app
• Data: Contains two CSV files disaster_messages.csv - Contains all the past messages & 'disaster_categories.csv' - contains the categories of the disaster messages
• Model: Contains the Machine Learning Pipeline python script file to perform all the training and testing of the data

List of Python libraries used

nltk sklearn numpy pandas sys sqlalchemy re pickle json flask

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://127.0.0.1:8080/

4. Link to the Github repo: https://github.com/mrinal1704/Disaster-Response-Pipeline-Project

Snapshots