[asd]

Simple project description.

Este é um modelo de classificação binário que busca diagnosticar a partir características comportamentais se uma criança possuí autismo.

Autores

Role	Responsibility	Full name	e-mail
Data Scientist	Author	[Manoel Vilela]	[manoel_vilela@engineer.com]
Data Scientist	Author	[Denilson Gomes]	[denilsongomes@alu.ufc.br]
Data Scientist	Author	[Matheus Frota]	[matheusfrota1234@gmail.com]

Usage

Describe how to reproduce your model

Usage is standardized across models. There are two main things you need to know, the development workflow and the Makefile commands.

Both are made super simple to work with Git and Docker while versioning experiments and workspace.

All you'll need to have setup is Docker and Git, which you probably already have. If you don't, feel free to ask for help.

Makefile commands can be accessed using make help.

Make sure that docker is installed and you have access to S3.

Clone the project from the analytics Models repo.

git clone ssh://git@github.com:datascience-ufc/asd.git
cd asd

Set the ssh auth variables:

eval $(ssh-agent)
ssh-add ~/.ssh/your-secret-key

Load cloud data & run the project

make load VERSION=X.Y.Z # load data + models
make run # generate a new model

After that, if you want test the IRIS dataset you can predict the class for new labels using for instance the workspace/data/test.csv:

make predict INPUT=workspace/data/test.csv

You access your workspace in the file predict/output.csv.

Folder structure

Explain you folder strucure

• docs: contains documentation of the project
• analysis: contains notebooks of data and modeling experimentation.
• tests: contains files used for unit tests.
• asd: main Python package with source of the model.