penguin-predictor

Description

The project aims to create a model that predicts the penguin species based on specific attributes.

The penguins dataset was used, consisting of the following data:

• species
• island
• bill length
• bill depth
• flipper length
• body mass
• sex

An application utilizing a machine learning engine, designed for easy portability across various environments, straightforward deployment in diverse settings, and structured with separated operational logic.

The project used technologies such as: Fast API, Kedro, Swagger and libraries such as: Scikit-learn and autogluon.tabular.

Running the project

To run the project, you need to perform the following steps:

1. Create conda environment

conda env create -f environment.yml

2. Activte the environment

conda activate penguins-env

3. Run the project

kedro run 

PyCharm Setup

Here's a quick guide to setting up PyCharm as a development environment for working on Kedro projects.

Setup link

Overview

This is your new Kedro project, which was generated using kedro 0.18.13.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

pip install -r src/requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

kedro test

To configure the coverage threshold, go to the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

kedro build-reqs

This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.

After this, if you'd like to update your project requirements, please update src/requirements.txt and re-run kedro build-reqs.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, catalog, and startup_error.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r src/requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:

kedro jupyter convert --all

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project

Docker Configuration

Prerequisites

Make sure you have Docker installed on your system. If not, you can download it here.

Building the Docker Image

To build the Docker image, run the following command in the project root directory:

docker build -t your-image-name .

Scikit-learn library instalation

pip install -U scikit-learn

Autogluon library installation

pip install autogluon