Disaster Response Pipeline Project

Description

In this project, you analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

The Project Workspace contains real messages that were sent during disaster events.

The project includes a simple flask web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data.

Some screenshots:

Prerequisites

All scripts and python source codes are tested under osx and linux only.

Initialize the python environment

You do not want to polute your local environment with new python libraries. Instead you should create a virtual python environment in the current directory. The only requirement is that python3 is preinstalled on your local system. Then just run:

python3 -m venv venv

And activate the virtual environment in your current shell:

source ./venv/bin/activate

Now you can install the needed python libraries:

pip install --upgrade pip
pip install pylint
pip install autopep8
pip install pandas
pip install jupyterlab
pip install matplotlib
pip install plotly
pip install scikit-learn
pip install nltk
pip install SQLAlchemy
pip install flask

Instructions:

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

   • To run ETL pipeline that cleans data and stores in database

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

   • To run ML pipeline that trains classifier and saves

     python train_classifier.py data/DisasterResponse.db models/model.pkl

2. Run the following command in the app's directory to run your web app. python run.py

3. Go to http://127.0.0.1:3001/