There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.
To help emergency workers in different organizations make quick response for people who need help, the project create a web app to classify a new message in several categories, such as water, food, medical supplies. To classify the message into several categories, I trained a multioutput random forest classifier.
The data are real messages that were sent during disaster events provided by Figure Eight. There are two csv files called disaster_messages.csv and disaster_categories.csv in data folder.
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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 -
Go to http://0.0.0.0:3001/