The following packages (i.e., libraries) are necessary to successfully run this project on your local machine:
- python >=3.6
- numpy >= 1.19.2
- pandas >= 1.0.1
- scikit-learn >= 0.20
- nltk >= 3.0
-
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
-
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/
To showcase data engineering skills and expand your opportunities and potential as a data scientist, in this project, you'll apply data engineering skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. Real messages that were sent during disaster events will be analyzed. You will be creating a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.
Your project will include a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data. This project will show off your software skills, including your ability to create basic data pipelines and write clean, organized code!
Just download (or git-clone) this project and use it with anaconda :)
Copyright (c) 2021 Vagner Zeizer C. Paes
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Udacity is strongly and the reviewers are highly acknowledged for this great experience of writting a Data Science Blog.
- Vagner Zeizer Carvalho Paes
This project is part of the Introduction to Machine Learning with Tensorflow Nanodegree.