https://github.com/ericshantos/playground

Repository of projects and practical experiments in computing, including Machine Learning and other applications.
https://github.com/ericshantos/playground

computer-science deep-learning machine-learning

Last synced: 12 months ago
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Repository of projects and practical experiments in computing, including Machine Learning and other applications.

• Host: GitHub
• URL: https://github.com/ericshantos/playground
• Owner: ericshantos
• License: mit
• Created: 2025-05-10T22:35:12.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-18T20:55:21.000Z (about 1 year ago)
• Last Synced: 2025-05-18T21:32:54.829Z (about 1 year ago)
• Topics: computer-science, deep-learning, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.66 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
[🇧🇷] [Lê em português](README.pt.md)

# Welcome to the PlayGround 🚀

This repository aims to store projects and experiments in computing, including Machine Learning (ML) models and other related practices. Here you will find various examples of algorithms, experiments, and tests conducted on different computational problems. 😊

## What will you find in this repository? 📂

This repository serves as a space for study and experimentation, with a collection of Jupyter notebooks and projects where I document the concepts I'm studying and testing. Each project is an opportunity to apply techniques, from the simplest to the most advanced.

### Main Components:

#### **Projects and Notebooks** 📓

This repository contains various projects and notebooks with algorithms and models I’m learning. Each file includes the code to implement a solution, along with explanations of how it works and the steps involved in each experiment.

#### **Datasets** 🗂️

Some projects use public and ready-to-use datasets. These datasets allow you to test and apply different techniques without needing to search for external sources.

#### **Models and Algorithms** 🧠

The repository includes everything from basic algorithms to more complex models, covering Machine Learning and other areas of computing. Each implementation is accompanied by a detailed study and improvement attempts.

## Available Projects

Below is a table with the available projects in the repository:

| Project Name                                                   | Algorithm Type                     | Library/Framework                                                                                                          |

| -------------------------------------------------------------- | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |

| [boston\_housing](./projects/boston_housing/)                  | Machine Learning (Regression)      | ![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge\&logo=scikit-learn\&logoColor=white) |

| [client\_marketing](./projects/client_marketing/)              | Machine Learning (Regression)      | ![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge\&logo=scikit-learn\&logoColor=white) |

| [specie\_flowers](./projects/specie_flowers/)                  | Machine Learning (Clustering)      | ![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge\&logo=scikit-learn\&logoColor=white) |

| [product\_value\_forecast](./projects/product_value_forecast/) | Machine Learning (Clustering)      | ![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge\&logo=scikit-learn\&logoColor=white) |

| [MNIST\_Predictor](./projects/MNIST_Predictor/)                | Machine Learning (Neural Networks) | ![Keras](https://img.shields.io/badge/Keras-D00000?style=for-the-badge\&logo=Keras\&logoColor=white)                       |

## How to use this repository? 🛠️

To start using the repository, follow the steps below:

1. **Clone the repository**

   To get a local copy, run:

   ```bash

   git clone https://github.com/your-username/playground.git

   ```

2. **Install the dependencies**

   To install the required libraries, use:

   ```bash

   pip install -r requirements.txt

   ```

3. **Open the notebooks or projects**

   To explore the Jupyter notebooks, run:

   ```bash

   jupyter notebook

   ```

4. **Explore!**

   Browse through the projects, run the code, and follow the provided explanations.

## What else can you learn here? 📚

In addition to Machine Learning, this repository offers an opportunity to learn about:

* Data preprocessing techniques.

* Classification, regression, and clustering algorithms.

* Performance evaluation with metrics like accuracy, precision, and recall.

* Other computing practices, such as software development, automation, and data analysis.

## Contribute! 🤝

If you have suggestions for improvements or found something that can be enhanced, feel free to open an **issue** or submit a **pull request**. All contributions are welcome!

## License 📜

This repository is licensed under the **MIT** license. For more details, see the [LICENSE](LICENSE) file.