🍷 Wine Quality Classifier

A machine learning–powered web application that assesses the quality of wine based on its physicochemical properties. Built with Flask, Scikit-Learn, and Pandas, this project blends oenology with machine learning to create an interactive tool for wine quality analysis. This project demonstrates how data science can be applied to the food and beverage industry to standardize quality assessment.

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🚀 Overview

The Wine Quality Classifier uses a trained Machine Learning Model (optimized via GridSearchCV) to predict whether a given wine sample is of Low, Average, or High quality. The model is hosted via a Flask web app where users can input parameters (like acidity, sugar, and alcohol content) and get instant quality assessments.

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🧠 Motivation

Wine tasting is traditionally a subjective art, reliant on the refined palates of sommeliers. However, the chemical composition of wine plays a decisive role in its quality. This project aims to bridge the gap between chemistry and sensory experience, providing an objective, data-driven approach to classifying wine quality.

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📊 Dataset

• Source: Wine Quality Dataset (WineQT)
• Classes:
  • Low → 0 (Quality 3, 4)
  • Average → 1 (Quality 5, 6)
  • High → 2 (Quality 7, 8)
• Features:
  • Fixed Acidity, Volatile Acidity, Citric Acid
  • Residual Sugar, Chlorides
  • Free & Total Sulfur Dioxide
  • Density, pH, Sulphates, Alcohol

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⚙️ Model Architecture

The model generation pipeline (fit.py) includes the following steps:

Step	Description
Imputation	Missing values handled using median strategy
Scaling	Standardized with StandardScaler
Augmentation	SMOTE (Synthetic Minority Over-sampling Technique) for class balance
Dimensionality	PCA (Principal Component Analysis) for feature reduction
Classifier	Optimized Estimator (Random Forest / XGBoost / KNN) found via GridSearchCV

Final model artifacts are serialized with joblib as:

models/
├── model.pkl
└── columns.pkl

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🧩 Project Structure

Wine_Research/
├── Datasets/
│   └── WineQT.csv           # Primary dataset
├── models/
│   ├── columns.pkl          # List of feature names
│   └── model.pkl            # Serialized trained model
├── reports/
│   └── research.html        # Static HTML report of the research notebook
├── static/                  # Static assets
│   ├── script.js            # Frontend interaction logic
│   └── style.css            # Premium styling
├── templates/
│   └── index.html           # Main web interface
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├── .gitignore               # gitignore file
├── app.py                   # Flask application entry point
├── fit.py                   # Machine learning pipeline script
├── LICENSE                  # Licensing information
├── research.py              # Research and analysis script
└── requirements.txt         # Dependencies

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💻 Installation & Usage

1️⃣ Clone the Repository

git clone https://github.com/ByteBard58/Wine_Research.git
cd Wine_Research

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the App

python app.py

The app will start at http://127.0.0.1:5000.

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🐳 Run the app directly via Dockerhub Image

A Dockerhub repository is created where I have pushed the docker image which contains the entire Wine_Research app.

The image is built on both ARM64 and AMD64 architectures, so that it can run on almost all major computers. You can run the app easily by using the Dockerhub Image. Here's how you can do it:

1. Install Docker Desktop and sign-in. Make sure the app is functioning properly.

2. Open Terminal and run:

docker pull bytebard101/wine_research:latest
docker run --rm -p 5000:5000 bytebard101/wine_research:latest

3. If your machine faces a port conflict, you will need to assign another port. Try to run this:

docker run --rm -p 5001:5000 bytebard101/wine_research:latest

If you followed Step 2 and the command ran successfully, then DO NOT follow this step.

4. The app will be live at localhost:5000. Open your browser and navigate to http://127.0.0.1:5000 (or http://127.0.0.1:5001 if you followed Step 3).

Check Docker Documentation to learn more about Docker and it's commands.

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🍷 Web Interface

Users can input physicochemical parameters such as:

• Acidity levels (Fixed, Volatile, Citric)
• Sugar & Salt (Residual Sugar, Chlorides)
• Sulfur Dioxide levels
• Density & pH
• Alcohol content

The app returns the predicted quality tier (Low, Average, High) with a corresponding visual indicator.

Note: Due to the absence of front-end web development expertise and the lack of other contributors for the project, I had to resort to using AI tools (such as LLM services like ChatGPT™, Grok™, and GitHub Copilot™) to create a sophisticated front-end for the web app.

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🧰 Tech Stack

• Languages: Python, HTML, CSS, JavaScript
• Libraries: Flask, Scikit-Learn, Pandas, NumPy, Joblib, XGBoost, Imbalanced-Learn
• Dataset Source: WineQT (Kaggle)

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🪐 Author

Sakib ( ByteBard58 )

Student | Aspiring Computer Engineer | AI & ML Enthusiast

📍 GitHub Profile: ByteBard58

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😃 Appreciation

Thank You for taking the time to review my work. I hope you enjoyed it and found it interesting. It would mean a lot to me if you could star it on GitHub 🌟

If you have any questions, suggestions, or anything you’d like to discuss, please don’t hesitate to reach out. You can find my contact information on my GitHub profile page. I’m all ears! 😊

Have a great day !