https://github.com/bomada/disaster-response-project

Analyse disaster data from Figure Eight to build a model for an API that classifies disaster messages.
https://github.com/bomada/disaster-response-project

ml portfolio python

Last synced: about 1 year ago
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Analyse disaster data from Figure Eight to build a model for an API that classifies disaster messages.

• Host: GitHub
• URL: https://github.com/bomada/disaster-response-project
• Owner: Bomada
• License: mit
• Created: 2019-06-19T18:34:53.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2019-06-20T20:17:16.000Z (about 7 years ago)
• Last Synced: 2025-04-04T02:41:22.673Z (over 1 year ago)
• Topics: ml, portfolio, python
• Language: Python
• Size: 2.02 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Disaster Response Pipeline Project

Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. The data set contain real messages that were sent during disaster events. Code create a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency. Project also include a web app where an emergency worker can input a new message and get classification results in several categories. The web app also display visualizations of the training data.

### Table of Contents

1. [Instructions](#instruction)

2. [File Descriptions](#file)

3. [Licensing, Authors, Acknowledgements](#license)

### 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 it into a .pkl file

    `python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl`

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

    `python run.py`

3. Go to http://0.0.0.0:3001/

### File Descriptions 

- **app**

    - **templates/go.html**: Web page that handles user query and displays model results

    - **templates/master.html**: Web page with plotly graphs

    - **run.py**: Run web application

- **data**

    - **disaster_categories.csv**: Data for categories which are the target variables

    - **disaster_messages.csv**: Data for messages which features are built from

    - **process_data.py**: Run ETL pipeline that cleans data and stores in database

- **models**

    - **train_classifier.py**: Run ML pipeline that trains classifier and save model into .pkl file

### Licensing, Authors, Acknowledgements 

Big thank you to Udacity for providing the template code for this project. Also want to thank Figure Eight for providing the data.