Objective: The objective is to create a disaster response pipeline.
It has three components in total
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ETL module: It takes data from disaster_categories.csv and disaster_messages.csv (dataset). Code: Process_data.py - Reads in the data, cleans and stores it in a SQL database.
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Model module: The Train_classifier.py file includes the code necessary to load data the sql database, transform it using natural language processing and machine language algorithm. Has GridSearchCV and can train and test the model
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Web App Module: The run.py has the user interface needed to predict and display results. The templates folder contains the html template
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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
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Go to http://0.0.0.0:3001/ (Tip: after executing step 1 and 2)