Disaster Response Pipeline Project

This project is part of the Udacity's Data Scientist Nanodegree.

Overview

The goal of this project is to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

Python libraries

• sys
• pandas
• nltk
• sqlalchemy
• sklearn
• pickle

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 it as a pickle file:

     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/

Web app instructions

1. You have to enter your messsage in the appropriate box:

2. The categories assigned to your message will appear highlighted:

Files

• data/process_data.py: python script that reads two csv files (the messages file and the categories files) and creates a SQL database with a cleaned table.

• data/disaster_messages.csv: csv file with the messages.

• data/disaster_categories.csv: csv table with the categories for each message.

• data/DisasterResponse.db: output of the process_data.py, you don't need this file to start.

• models/train_classifier.py: python script that reads the SQL database and creates and trains a classifier, and stores it in a pickle file.

• app/run.py: python scripts that runs the app.

• app/templates/* : templates for the app.

License

Acknowledgements

• Udacity: for the idea and the starter code.
• Figure Eight: for the labeled data set.