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Disaster-Response

This project seek to predict the message from disaster in multicategories.

Files

- app
| - template
| |- master.html  # main page of web app
| |- go.html  # classification result page of web app
| |- webpage_print.png # print 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
|- emergency.db   # database to save clean data to

- notebooks
|- etl_pipeline.ipynb  # pipeline with etl  
|- ml_pipeline.ipynb  # pipeline with ml process

- models
 |- train_classifier.py
 |- classifier.pkl  # saved model

Requirements

  • scikit-learn==0.19.1
  • pandas==0.23.3
  • numpy==1.12.1
  • nltk==3.2.5
  • lightgbm==3.1.1

How to use

In a terminal, use the follow command to execute etl pipeline:

python process_data.py disaster_messages.csv disaster_categories.csv DisasterResponse.db

The follow command will train your model:

python train_classifier.py ../data/DisasterResponse.db classifier.pkl

To run the app:

python run.py

Ackowledgements:

Udacity and their mentors the help to develop this project. Also, the data provided by FiveThirtyEight

disaster-response's People

Contributors

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