Disaster Response Pipeline Project

This project employs ETL and ML pipelines to analyze disaster data from Appen to build a model for an API that classifies disaster messages. I built a machine learning pipeline to categorize real-world communications submitted during disasters so that they may be sent to the proper disaster aid organization.

The project comprises a web interface via which an emergency worker may enter a new message and receive categorization results in a variety of categories. The online app will also offer data visualizations.

File Descriptions:

The project contains the following files,

• ETL Pipeline Preparation.ipynb: Notebook for the ETL pipeline
• ML Pipeline Preparation.ipynb: Notebook for the machine learning pipeline
• models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle (due to size constraints on github, pickle could not be uploaded).
• tokenizer: function to apply nlp modifications to the ML pipelines
• app/templates/~.html: HTML pages for the web app.
• run.py: Start the Python server for the web app and prepare visualizations.

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. Go to app directory: cd app

3. Run your web app: python run.py

4. Click the PREVIEW button to open the homepage

web snapshots

Credits

• Access bank
• Udacity