Disaster Response Pipeline Project

Running instructions for linux environment

Prerequisite to have been installed.

01. Python3.6(Python3)
02. Virtualenv
03. Requirements that are in requirements.txt

Step 01: Clone the project to your workplace

If you are using a ssh key for GitHub, please use flowing git command to clone the project.

git clone git@github.com:snandasena/disaster-response-pipeline.git

Otherwise use HTTPS git method to clone the git repositories.

git clone https://github.com/snandasena/disaster-response-pipeline.git

Step 02: Create a Python3.6(Python3) virtual environment inside the project root directory

Here I assumed you are in the project root directory and here "." is represent the current directory.

virtualenv -p python3.6 .

Then activate the python virtual environment

source bin/active

Run application with a single command in linux

chmod 755 run.sh
./run.sh

Description for run.sh

#!/bin/bash
# to install required python dependencies
python -m pip install -r requirements.txt

# to install nltk packages
python -m nltk.downloader wordnet
python -m nltk.downloader stopwords
python -m nltk.downloader punkt

# to run ETL pipeline
rm -rf ./data/DisasterResponse.db >/dev/null
#
python process_data.py ./data/disaster_messages.csv ./data/disaster_categories.csv ./data/DisasterResponse.db

# to run ML pipeline

rm -rf ./models/classifier.pkl >/dev/null
python train_classifier.py ./data/DisasterResponse.db ./models/classifier.pkl

# run the application
# shellcheck disable=SC2164
python run.py ./data/DisasterResponse.db ./models/classifier.pkl

Note: To enable hyper parameter tuning please change the following code line

From:

model = build_model()

To:

model = build_model(enable_param_tuning=True)

in train_classifier.py

After ETL and ML pipeline completion, the app will be started automatically.
Then go to web browser and hit the this url http://localhost:3001/.

Acknowledgments

Licensing, Authors, Acknowledgements

Big thank you to Udacity for providing the template code for this project. Also want to thank Figure Eight for providing the data.