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Update of README.md written by Ola
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# penguins

## Runing the project
## Description

1. Create conda envirement
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 envirement
2. Activte the environment

```
conda activate penguins-env
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## PyCharm Setup

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

[Setup link](https://docs.kedro.org/en/stable/development/set_up_pycharm.html)

## Overview
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## Package your Kedro project

[Further information about building project documentation and packaging your project](https://docs.kedro.org/en/stable/tutorial/package_a_project.html)


## Docker Configuration

### Prerequisites
Make sure you have Docker installed on your system. If not, you can download it [here](https://www.docker.com/get-started).

### 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
```

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