Continuously Integrated Disaster Response Application

Project Description:

In this project, I built a data transformation - machine learning pipeline that is capable to curate the class of the messages. The pipeline is eventually built into a flask application. The project include a web app where an emergency worker can input a new message and get classification results in several categories. The landing page of the webapp also includes 4 visualizations of the training dataset built with plotly.

File Descriptions:

The project contains the following files,

• ETL Pipeline Preparation.ipynb: Notebook experiment for the ETL pipelines
• ML Pipeline Preparation.ipynb: Notebook experiment for the machine learning pipelines
• data/process_data.py: The ETL pipeline used to process data in preparation for model building.
• model/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle (pickle is not uploaded to the repo due to size constraints on github).
• app/templates/~.html: HTML pages for the web app.
• run.py: Start the Python server for the web app and prepare visualizations.

The app is now deployed on heroku at this link

Example message to classify: "Help, Fire!"

Local 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 model/train_classifier.py data/DisasterResponse.db model/classifier.pkl

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

3. Go to http://127.0.0.1:5000/