In this Workspace, you'll find a data set containing real messages that were sent during disaster events. A machine learning pipeline is create to categorize these events so that you can send the messages to an appropriate disaster relief agency.
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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/
There are three components for this project.
In a Python script, process_data.py, write 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, write 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
Data visualizations using Plotly in the web app.
Project is created with Python 3.6.9.
- NumPy
- Matplotlib
- pandas
- NLTK
- [joblib]
- [sqlalchemy]
- [string]
- [flask]
- [plot.ly]
- Mail: isaaccohensabban_at_gmail_dot_com