VinoPredict | Modern Enology AI 🍷

VinoPredict is an end-to-end Machine Learning ecosystem designed to analyze, predict, and archive red wine quality metrics with professional-grade precision. It combines a rigorous Python-based analytical pipeline with a stunning, interactive web dashboard powered by a real-time AI backend.

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✨ Key Features

🖥️ Interactive Web Application (VinoPredict)

• AIA Assessment Engine: Perform real-time AI quality evaluations based on 11 chemical parameters.
• Random Sampling: Instantly load historical data from the UCI Enology Repository for testing.
• Wine Vault: A comprehensive digital cellar containing 1,600+ historical records.
• Molecular Analysis Visualization: Dynamic progress rings and status badges (Reserve, Premium, Table).

🧠 Full-Stack AI Core

• Integrated Backend: A Python Flask server that bridges the frontend to the Machine Learning models.
• Ensemble Intelligence: Utilizes a sophisticated ensemble of Random Forest, Extra Trees, and Gradient Boosting (wine_quality_ensemble.pkl).
• Real-Time Inference: Processes chemical data through a production-ready feature scaler (scaler.pkl) for 93.75% accurate predictions.
• Hybrid Architecture: Includes a local heuristic fallback mechanism to ensure uptime even without backend connectivity.

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🛠️ Tech Stack

• Backend: Python 3.x, Flask, Joblib (Model Serialization).
• Frontend: Vanilla Javascript (ES6+), Modern CSS3 (Glassmorphism, Dynamic Animations), SVG dynamic components.
• AI/ML Core: Scikit-Learn, Pandas, NumPy.
• Dataset: UCI Red Wine Quality Dataset.

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🚀 Getting Started

1. Install Dependencies

Ensure you have the required Python libraries installed:

pip install flask joblib pandas scikit-learn

2. Run the Application

To launch the integrated server and interactive dashboard:

On Windows (PowerShell):

./run_local.ps1

On Bash/Linux/macOS:

chmod +x run_local.sh
./run_local.sh

The application will be available at: http://localhost:8080

3. Analytical Deep-Dive

To explore the model training process and data science research:

1. Open wine_quality_analysis.ipynb in Google Colab or Jupyter.
2. Run all cells to see the full EDA and model benchmarking.

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📈 Results

• Model Accuracy: Reached peak performance of 93.75%.
• Key Driver: Alcohol content remains the most significant predictor of quality in the UCI dataset.
• Efficiency: Includes train_optimized.py for rapid retraining and production-ready model export.

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Developed for the modern sommelier and data scientist.