Portfolio project to showcase Data Engineering skills including ETL and ML Pipeline preparation, utilising model in a web app, and data visualisation.
Instructions:
-
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 -
Go to http://0.0.0.0:3001/
Important Files:
data/process_data.py: The ETL pipeline used to process data in preparation for model building.models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle (pickle is not uploaded to the repo due to size constraints.).app/templates/*.html: HTML templates for the web app.run.py: Start the Python server for the web app and prepare visualizations.