Disaster Response Pipeline Project

A machine learning project part of Udacity's Data Scientist nanodegree program. The aim of this project is to analyze short messages and classify them according to various disaster-relief related categories, with a potential use for this being to forward said messages to the appropriate response agencies.

This product was written mostly in python, with html, css, and javascript integration using flask.

Libraries and Modules Used:

python:

• sys
• re
• numpy
• pandas
• sqlalchemy
• progressbar2
• nltk
• scikit-learn
• flask
• plotly
• joblib

File Structure:

disaster-response-pipeline/
├── app/
│ ├── templates/
│ │ ├── go.html
│ │ └── master.html
│ └── run.py
├── data/
│ ├── disaster_categories.csv
│ ├── disaster_messages.csv
│ ├── DisasterResponse.db
│ └── process_data.py
├── models/
│ └── train_classifier.py
└── README.md

How to run:

1. Run the following commands in the project's root directory to set up your database and model.

   • To run the 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 the 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 directory to run your web app. python run.py

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