Disaster Response Pipeline

The goal of this project is to accurately classify which responses needs attention during emergency situation. A web dashboard is made to solve this probloem.

Content

• Data
  • process_data.py: Reads in the dataset, cleans and processes it and stores it in a SQL database.
  • disaster_categories.csv(Dataset Containing 36 types of categories) and disaster_messages.csv(Messages during a disaster)
  • DisasterResponse.db: Output file from process_data.py. Stores the processed data in a database.
• Models
  • train_classifier.py: Loads the DisasterResponse.db and does text cleaning before feeding the output to a machine learning pipeline using Random Forests. GridSearchCV is used to tune the hyperparametets. Model is saved as a pickle file.
  • classifier.pkl: Saved Machine Learning model
• App
  • run.py: Flask app and the GUI used to predict results and display them. Uses classifier.pkl as model.
  • templates: Folder containing the html templates

Usage:

python process_data.py disaster_messages.csv disaster_categories.csv DisasterResponse.db

python train_classifier.py ../data/DisasterResponse.db classifier.pkl

python run.py

Go to http://0.0.0.0:3001 to visualize the Web App

Screenshots

Homepage:

Example 1:

Example 2:

About

The data was provided by Figure Eight as a part of the Udacity Data Scientist Nanodegree programme.