This project build ETL & Machine Learning pipelines to build a model for an API that classifies disaster messages
Disaster text messages from Figure Eight
- disaster _categories.csv
- disaster_messages.csv
- Built ETL pipeline with Python that extract, clean and load the emergency messages to a SQLite database
- create a machine learning pipeline that uses NLTK, as well as scikit-learn's Pipeline and GridSearchCV to output a final model that uses the message column to predict classifications for 36 categories (multi-output classification)
- Create a Flask App that contain data visulizations and allow users to use the trained model to classify new messages.
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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/