Disaster Response Pipeline Project
Introduction
As a Udemy Data Scientist Nanodegree Program student, I'm tasked with solving the Disaster Response Pipeline Project and publishing the results.
This project aims to revolutionize disaster response by developing an intelligent system that rapidly categorizes and routes incoming messages to appropriate relief agencies. Using advanced NLP and machine learning, it provides instant multi-category classification through a user-friendly web interface, enabling swift and efficient resource allocation. The goal is to significantly improve disaster management effectiveness, ultimately saving more lives and minimizing crisis impact through data-driven response strategies.
This project applies data engineering skills to analyze disaster data from Appen and build a model for an API that classifies disaster messages. The main components include:
- ETL Pipeline: Processes and cleans disaster message data, storing it in a SQLite database.
- ML Pipeline: Develops a machine learning model to categorize disaster messages.
- Flask Web App: Provides an interface for emergency workers to input new messages and receive classification results.
Key features:
- Real-time classification of disaster messages
- Data visualizations of the disaster response data
- Utilizes NLP and machine learning techniques
The project showcases:
- Data pipeline development
- Machine learning model creation
- Web application deployment
- Clean, organized code structure
The notebook and source code are available here:
Repository: https://github.com/anibalsanchez/disaster-response-pipeline-project
