In this project I have built up a Web application which takes input messages during calamity and direct to a particular alleviation organization that can give speedy help
The application utilizes a ML model to sort any new messages got, and the store additionally contains the code used to train the model and to set up any new datasets for model training purposes. Read the entire blog at: https://towardsdatascience.com/building-a-disaster-response-web-application-4066e6f90072
Data Source: Figure Eight

• Workspace: This is the main folder containing all the sub folders and files.
• Data: This folder contains all the .csv files, .db file and .py file
• Data-> disaster_categories.csv/disaster_messages.csv: These files inside the data folder contains messages, their genres and different categories they beong to.
• Data-> process_data.py: This code takes as its input csv files containing message data and message categories (labels), and creates an SQLite database containing a merged and cleaned version of this data
• Data-> disaster.db: This file is the database which is used to fetch data whenever needed
• Models: This folder contains the ML pipeline and the pickle object
• Models-> train_classifier.py: This code takes the SQLite database produced by process_data.py as an input and uses the data contained within it to train and tune a ML model for categorizing messages. The output is a pickle file containing the fitted model. Test evaluation metrics are also printed as part of the training process.
• Models->classifier.pkl: This file contains the fitted model so we do not need to fit the model again
• App: This folder contains run.py and templates which are used to run the main web application

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/ or localhost:3001

This image shows how the message "Due to heavy rains, there's flood in Mumbai. Help neeeded" is classified into different categories like Related, aid related, search and rescue..