[predict-actions-price]

Simple project description.

This model is a type_of_modeling that do_something to solve this_problem aimed to the clients of the some_vertical.

Stakeholders

Describe the people involved in this project

Role	Responsibility	Full name	e-mail
Data Scientist	Author	[Matheus Frota]	[[email protected]]
Project Owner	Co-author	[First name Last Name]	[e-mail]

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.

Clone the project from the analytics Models repo.

git clone https://github.com/<@github_username>/predict-actions-price.git
cd predict-actions-price

Final Report (to be filled once the project is done)

Model Frequency

Describe the interval frequency and estimated total time to run

Model updating

Describe how your model may be updated in the future

Maintenance

Describe how your model may be maintained in the future

Minimum viable product

Describe a minimum configuration that would be able to create a minimum viable product.

Early adopters

Describe any potential paying users for this product if it was available today. Also state a point of contact for each of them.

Documentation

• project_specification.md: gives a data-science oriented description of the project.

• model_report.md: describes the modeling performed.

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.
• predict_actions_price: main Python package with source of the model.