This project seek to predict the message from disaster in multicategories.
- 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
- scikit-learn==0.19.1
- pandas==0.23.3
- numpy==1.12.1
- nltk==3.2.5
- lightgbm==3.1.1
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
Udacity and their mentors the help to develop this project. Also, the data provided by FiveThirtyEight