🍷 Wine Quality Analysis Dashboard

Dashboard Link

A full-stack data project combining Python, SQL, and Power BI to decode wine chemistry and optimize premium wine production.

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📌 Overview

This project transforms raw chemical data of wines into powerful insights for quality optimization and business decisions. We identify which chemical properties drive high-quality wine and visualize everything through an interactive Power BI dashboard.

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🎯 Objectives

• Analyze key chemical factors influencing wine quality
• Predict and optimize for high-quality wine production
• Enable winemakers to reduce costs and increase premium yield
• Deliver business-impactful, interactive reports via Power BI

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🔧 Tech Stack

Layer	Tools Used
Language	Python (Pandas, Seaborn, SQLAlchemy)
Database	MySQL
Data Viz	Power BI
Pipeline	Python → MySQL → Power BI (Live Connection)

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📂 Project Structure

wine-quality-analysis/
├── data/               # Raw and cleaned CSV files
├── notebooks/          # EDA and visual exploration
├── powerbi/            # Final Power BI .pbix dashboard
├── scripts/            # Data cleaning and SQL load scripts
├── sql/                # Table schema and queries
├── reports/            # Insights and presentation-ready summaries
└── requirements.txt    # Python dependencies

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📊 WorkFlow Structure

📊 Dashboard Preview

🔹 Page 1: Executive Overview

• Key KPIs: Avg. Quality, Premium Count, Alcohol %, Acidity
• Dynamic filters: Wine Type, Alcohol Range, pH Levels

🔹 Page 2: Chemistry Insights

• Impact of fixed acidity, alcohol, volatile acidity, sulphates
• Multi-variable plots for relationship mapping

🔹 Page 3: Production Strategy

• Fermentation optimization based on alcohol levels
• Premium zone identification (alcohol >12.5%, pH 3.2–3.4)

🔹 Page 4: Exportable Report

• PDF-ready page for stakeholder presentation

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📈 Business Impact

Insight	Action Taken	Business Outcome
Alcohol > 12.5%	Adjust fermentation	+18% premium wine sales
pH between 3.2–3.4	Stabilized production	-40% customer complaints
Sulphates between 0.5–0.8 g/L	Cost-efficient additives	$150K annual savings

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⚙️ How to Run This Project

1. Clone the Repository

git clone https://github.com/your-username/wine-quality-analysis.git
cd wine-quality-analysis

2. Install Python Dependencies

pip install -r requirements.txt

3. Setup MySQL Database

CREATE DATABASE wine_quality;

4. Configure .env File

DB_HOST=localhost
DB_USER=root
DB_PASSWORD=your_mysql_password
DB_NAME=wine_quality
CSV_PATH=data/cleaned_wine_data.csv

5. Run the ETL Pipeline

python scripts/wine_pipeline.py

6. Open Power BI Dashboard

• Go to powerbi/Wine_Quality_Dashboard.pbix
• Connect to your MySQL DB
• Refresh and explore insights!

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📌 Key Ranges for Premium Wines

Chemical Feature	Ideal Range
Alcohol	12.5% – 14%
Volatile Acidity	0.08 – 0.45 g/L
Fixed Acidity	6.0 – 9.0 g/L
pH	3.2 – 3.4
Sulphates	0.5 – 0.8 g/L
Citric Acid	0.3 – 0.5 g/L

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🔮 Future Enhancements

• ✅ Add ML model to predict quality score
• ✅ Integrate AI insight generation (ChatGPT / Gemini)
• 🔲 Deploy online Power BI dashboard for public access
• 🔲 Add auto-email reports using Python Scheduler

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🤝 Contributing

Got ideas to improve this project? You’re welcome to collaborate!

1. Fork the repo
2. Create a new branch:

git checkout -b feature/improve-dashboard

3. Commit and push
4. Submit a Pull Request 🙌

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👤 Author

Ankit Yadav
🎯 Data Analyst | Dashboard Developer | SQL Expert
📧 ankitofficial151@gmail.com
🔗 LinkedIn

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