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git clone the repository
https://github.com/linnforsman/disaster-response-pipeline.git
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/
This project is part of the Data Scientist Nanodegree by Udacity in collaboration with Figure Eight. The dataset contains pre-labelled tweet and messages from real-life disaster events. The project aim is to build a Natural Language Processing model to categorize messages on a real time basis.
data/process_data.py
: This file contains the ETL pipeline that processes the raw data and stores it in the database.models/train_classifier.py
: This file contains the ML pipeline that trains the classifier and saves it to the database.app/templates/*.html
: This directory contains the html templates for the web app.run.py
: This file contains the flask app that runs the web app.app
| - template
| |- master.html # main page of web app
| |- go.html # classification result page of web app
|- run.py # Flask file that runs app
data
|- disaster_categories.csv # data to process
|- disaster_messages.csv # data to process
|- process_data.py
|- InsertDatabaseName.db # database to save clean data to
models
|- train_classifier.py
|- classifier.pkl # saved model
notebooks
|- ETL Pipeline Preparation.ipynb # Jupyter Notebook
|- ML Pipeline Preparation.ipynb # Jupyter Notebook
README.md
The data was provided by Figure Eight in collaboration with Udacity.
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