To summarise the process:
Built an ETL pipeline that process message and category data from csv file, and load them into SQLite database
which ML pieline will then read from to create and save a multi-output supervised learning model
Then web app will extract data from this database to provide data visualisation
and use model to classify new messages to 36 categories
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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
https://drive.google.com/open?id=1kXG1--5f2xtHzTkycy6mioRXkSx_6BOu for skipping the training part. It works in kaggle. For me , it didn't work in local computer . It gives a warning when used in local computer.
Notes and tips for this projects can be seen in help.txt file .