Disaster Response Pipeline Project

Summary:

The project is a set of tools for analyzing messages, received during Natural disasters events, like earthquakes, storm or floods.

Using Machine learning technics and already collected data, this project helps to identify a message and assign appropriate categories. Such categories allow to link a received message with an proper organization, which can help people in difficult situation.

There are three main parts in this project:

• ETL pipeline is for cleaning and preparing data for the next step.
• ML pipeline use the data from the previous step and creates a Machine learning model with the best parameters for message categorization.
• Web-app: a front-end of this project aimed to visualize data (used for ML model) and predicting categories for a new message.

As initial dataset, I used preheated and classified data from Figure Eight: https://www.figure-eight.com/dataset/combined-disaster-response-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's directory to run your web app. python run.py

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

Main files in the repository

• data/process_data.py - ETL pipeline used to prepare data for ML model building and save the date SQL file.
• models/train_classifier.py - ML pipeline used to train a model with the best parameters and save the trained model to pickle file
• app/run.py - Script to start a web-app for data visualization and message classification. This script uses the following templates:
  • app/templates/master.html - main template
  • app/templates/go.html - template for classification output