This project contains multilingual disaster response messages curated by Figure Eight. The data has been encoded with 36 different categories related to disaster response.
The repository contains working code for running an ETL pipeline, ML pipeline, and Flask app locally. Instructions are below.
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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 data/report.csv
- 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/
- data/process_data.py: ETL script to clean and load data into sqlite3 database
- models/train_classifier.py: builds, trains, evaluates, and saves machine learning classifier
- models/classifier.pkl: file that contains the saved machine learning model
- app/run.py: this file is used to run the Flask application
Use latest versions (as of October 8, 2019) for sklearn and plotly
sklearn v0.21.3- ensures that sklearn.metrics.classification report contains parameter output_dict in order to create classification report data table visualization
plotly v4.1.1- ensures that Table graph object is available