https://github.com/ndleah/disaster-response

⛰️ machine learning pipeline for disaster alert
https://github.com/ndleah/disaster-response

databases etl-pipeline flask-sqlalchemy machine-learning ml-engineering ml-pipeline

Last synced: 3 months ago
JSON representation

⛰️ machine learning pipeline for disaster alert

• Host: GitHub
• URL: https://github.com/ndleah/disaster-response
• Owner: ndleah
• Created: 2024-02-06T10:35:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-06T11:01:29.000Z (over 1 year ago)
• Last Synced: 2025-01-12T16:31:40.057Z (10 months ago)
• Topics: databases, etl-pipeline, flask-sqlalchemy, machine-learning, ml-engineering, ml-pipeline
• Language: Python
• Homepage:
• Size: 27.6 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
![Star Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F&message=If%20Useful&style=style=flat&color=BC4E99)

![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)

# Disaster Response Pipeline Project 

 ## Introduction

 This project will include a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data.







## ⚙️ 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 models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

        ```

2. Go to `app` directory: `cd app`

3. Run your web app: `python run.py`

4. Click the `PREVIEW` button to open the homepage

## 🚧 Folder Structure

```

- app

| - template

| |- master.html  # main page of web app

| |- go.html  # classification result page of web app

|- run.py  # Flask file that runs app

- data

|- disaster_categories.csv  # data to process 

|- disaster_messages.csv  # data to process

|- process_data.py

|- InsertDatabaseName.db   # database to save clean data to

- models

|- train_classifier.py

|- classifier.pkl  # saved model 

- README.md

```

## 📝 Feedback

If you have any feedback or ideas to improve this project, feel free to contact me via my email at nduongthucanh@gmail.com or:



  






  



___________________________________

© 2024 Leah Nguyen