README

The dataset contains pre-labelled tweet and messages from real-life disaster events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.

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 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/

Important Files:

• data/process_data.py: The ETL pipeline used to process data in preparation for model building.
• models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle (pickle is not uploaded to the repo due to size constraints.).
• app/templates: HTML templates for the web app.
• run.py: Start the Python server for the web app and prepare visualizations.
• data/process_data.py: The ETL pipeline used to process data in preparation for model building.
• models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle (pickle is not uploaded to the repo due to size constraints.).
• app/templates/*.html: HTML templates for the web app.
• run.py: Start the Python server for the web app and prepare visualizations.