https://github.com/huacenxu/disaster-response-pipeline

This project builds machine learning models to classify disaster messages and categorizes them accurately through a robust pipeline, ensuring effective classification.
https://github.com/huacenxu/disaster-response-pipeline

ai bootcamp-project classification-model data-science data-wrangling etl etl-pipeline

Last synced: 3 months ago
JSON representation

This project builds machine learning models to classify disaster messages and categorizes them accurately through a robust pipeline, ensuring effective classification.

• Host: GitHub
• URL: https://github.com/huacenxu/disaster-response-pipeline
• Owner: huacenxu
• Created: 2022-02-19T19:03:37.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-16T13:19:04.000Z (over 2 years ago)
• Last Synced: 2025-01-19T14:59:12.880Z (5 months ago)
• Topics: ai, bootcamp-project, classification-model, data-science, data-wrangling, etl, etl-pipeline
• Language: Jupyter Notebook
• Homepage:
• Size: 22.5 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Disaster Response Pipeline Project

## Project Motivation

In this project, I analyze disaster data from Figure Eight to build models that classify disaster messages. A machine learning pipeline is created to categorize these events to make sure that messages would be properly classified. Finally, I include a web app where an emergency worker can input a new message and get classification results in several categories.

## File Description

Editing here

Udacity Workspace Documents: 

![Screen Shot 2022-01-03 at 4 31 48 PM](https://user-images.githubusercontent.com/41206996/147985516-3655e96f-7d51-46d8-9948-6565f508a781.png)

ML pipeline notebook

ETL pipeline notebook

README.md

## Instruction

- 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 models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

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

- Go to http://0.0.0.0:3001/

## Acknowledgment 

This project benefits from the support from Udacity mentor and instructor team. I espcially appreciate Survesh's mentor help.