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.
Editing here Udacity Workspace Documents:
ML pipeline notebook
ETL pipeline notebook
README.md
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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
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Run the following command in the app's directory to run your web app. python run.py
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Go to http://0.0.0.0:3001/
This project benefits from the support from Udacity mentor and instructor team. I espcially appreciate Survesh's mentor help.