The goal of this project is to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. It also includes a web app where one can test the trained model by inputing a new message and getting a classification result.
The data set contains real messages that were sent during disaster events. There are 26216 messages categorized into 36 different categories.
app/run.py: contains the code to run the web appdata/process_data.py: contains the code to clean and store the data into a databasemodels/train_classifier.py: contains the code to train, tune and save a machine learning classifier
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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/