In the Project, I worked on a data set containing real messages that were sent during disaster events. I created a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.
This project also includes 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.
├── README.md
├── models
| ├── train_classifier.py
| └── classifier.pkl # saved model
├── data
| ├── process_data.py
| ├── disaster_categories.csv # data to process
| ├── disaster_messages.csv # data to process
| └── DisasterResponse.db # database to save clean data to
├── app
├── run.py
└── templates
├── go.html # classification result page of web app
└── master.html # main page of web app
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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
- To run ETL pipeline that cleans data and stores in database
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Run the following command in the app's directory to run your web app.
python run.py