Introduction

In this project, we aim at building an end-to-end machine learning application to classify the messages into 36 categories related to disaster. This is helpful to extract only disaster related from multiple media sources, so any appropriate disaster relief agency can be reached out for help.

Project components

Project is comprised of components: ETL pipeline, ML pipeline and Flass App.

Installation

To successfully run the project, below are list of dependencies need to be installed in the python environment:

• python >3.6
• sklearn==0.0
• nltk==3.5
• SQLAlchemy==1.3.22
• pandas==1.1.5
• numpy==1.19.4
• plotly==4.14.1
• Flask==1.1.2

ETL pipeline

• Perform Extract, Transform and Load data provided by Figure8 including messages and its corresponding categories.
• Target table will be stored in SQLite under table disaster_response
• Run python process_data.py messages.csv categories.csv disaster_response.db disaster_response in data directory to execute the ETL pipeline

ML pipeline

• Perform load data, build model, train, run cross validation for best param and export artifact model.
• Target output is classifier.pkl file which will be used later for prediction on Flask App.
• Run python train_classifier.py ./../data/disaster_response.db classifier.pkl in models directory to execute the ML pipeline

Flask app

• Visualize the report of the categories, message genre data

• Classify input message from dashboard

• Run python run.py in app directory to execute the Flask application

  The dashboard looks as below:

  

Licensing, Authors, Acknowledgement

Credits must be given to Figure Eight for the provided data.