🍷 Wine Quality Analysis - Python Data Science Project

This project is based on the final assessment from the UCD - Advanced Center (STAT40800) course.
It analyzes Portuguese red and white wine data using Python, applying data exploration, statistical analysis, and machine learning techniques.

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📂 Dataset Information

This project uses the Wine Quality Dataset from the UCI Machine Learning Repository.

The dataset consists of two CSV files:

• winequality-red.csv → Red wine data
• winequality-white.csv → White wine data

A full description of the dataset is available in winequality.names.

📌 Original Source:
UCI Wine Quality Dataset

📜 License:
This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
You are free to share and adapt the data as long as proper credit is given.

📌 Reference Paper:
Research Paper on Wine Data

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📊 Project Overview

🔹 Goal: Analyze and predict wine quality based on its physicochemical properties.
🔹 Techniques Used:

• Data cleaning & preprocessing
• Exploratory Data Analysis (EDA) with visualizations
• Statistical analysis (t-tests)
• Feature engineering & selection
• Machine learning models (Linear Regression, RandomForestRegressor)

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🛠️ Technologies Used

✔ Python (Pandas, NumPy, SciPy)
✔ Data Visualization (Matplotlib, Seaborn)
✔ Statistical Analysis (Statsmodels, Scipy.stats)
✔ Machine Learning (Scikit-Learn)
✔ Jupyter Notebook

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🚀 How to Run This Project

1️⃣ Clone the repository

git clone https://github.com/nznltn/Python-Final-Project.git
cd Python-Final-Project

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook

jupyter notebook

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📈 Project Structure

📂 Python-Final-Project  
 ├── 📁 data  
 │   ├── winequality-red.csv  
 │   ├── winequality-white.csv  
 │   └── winequality.names  
 ├── 📁 notebooks  
 │   ├── Wine_Quality_Analysis.ipynb  
 ├── 📁 src  
 │   ├── exploratory_analysis_and_preprocessing.ipynb  
 │   ├── machine_learning.ipynb  
 ├── README.md  
 ├── requirements.txt  

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📌 Future Improvements

🔹 Apply additional ML models (e.g., XGBoost)
🔹 Implement hyperparameter tuning for better predictions
🔹 Deploy a Streamlit dashboard for interactive visualizations

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💡 Contributing

If you have suggestions, feel free to submit a pull request or open an issue!

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📜 License

This project is open-source and available under the MIT License.

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👩‍💻 Author

Nazan Altun
📌 https://github.com/nznltn/