The project includes a web app where an emergency worker can input a new message and get classification results in several categories. The web app also displays visualizations of the data.
There are three main components for this project.
In a Python script, process_data.py, a data cleaning pipeline that:
- Loads the messages and categories datasets
- Merges the two datasets
- Cleans the data
- Stores it in a SQLite database
In a Python script, train_classifier.py, a machine learning pipeline that:
- Loads data from the SQLite database
- Splits the dataset into training and test sets
- Builds a text processing and machine learning pipeline
- Trains and tunes a model using GridSearchCV
- Outputs results on the test set
- Exports the final model as a pickle file
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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/
https://opensource.org/licenses/MIT
- Udacity for providing an amazing Data Science Nanodegree Program
- Figure Eight for providing the relevant dataset to train the model