Disaster Response Pipeline Project

In this project I will analyze disaster messages to build a machine learning model that classifies the messages using a web application so they can be sent to the relevant aid agencies.

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 a classifier 'python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl'

2. The app folder contains a web app ready to deploy to the Heroku platform, see an example here: https://disaster-response-pranath.herokuapp.com

Important Files

• data/process_data.py: The ETL pipeline used to process data in preparation for model building.
• models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle file.
• app/templates/*.html: HTML templates for the web app.
• app/run.py: Start the Python server for the web app and prepare visualizations.
• notebooks/*: Juypter notebooks used to develop ETL, NLP & ML pipelines

Results

1. In the first project finished in April 2019, the best result was an overall F1 score on all categories of 0.41
2. In the latest project finished in December 2019, the best result was an overall F1 score on all categories of 0.75

Presentations

This work was presented at the Data Science meetup 'Brighton Data Forum' on 29th May 2019

https://www.meetup.com/Brighton-Data-Forum/events/mgggbqyzhbfc/

The presentation slides for this can be found here:

https://docs.google.com/presentation/d/164oDiuEZFR35X9QEJN1R-rOb4sp5r32DxR4Xm6z-Qgs/edit#slide=id.p

Screenshots

Home page - stats view 1

Home page - stats view 2

Categorization results page