🌱 ZeroTrace

Carbon Emission Tracking & Analytics Platform

⭐ A full-stack carbon emissions analytics system built using Python, Flask, MySQL, and data visualization tools to analyze historical emissions and provide meaningful environmental insights.

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📌 Overview

ZeroTrace is a data-driven web application designed to track, analyze, and visualize carbon emission data across countries, sectors, and years. The project demonstrates strong fundamentals in data engineering, backend development, and analytics, using real-world datasets and structured storage.

The platform enables users to explore emission trends, compare regions, and understand environmental impact through interactive dashboards.

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🔥 Highlights

• 🌍 Historical carbon emission analysis
• 📊 Interactive charts & dashboards
• 🗄️ MySQL-based structured data storage
• 🧮 Emission calculation & data views
• ⚡ Fast data querying and visualization
• 📱 Clean, responsive web interface

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📁 Project Structure

ZeroTrace/
│
├── static/
│   ├── images/
│   │   ├── about.avif
│   │   ├── blogbg1.jpg
│   │   ├── calbg2.jpg
│   │   ├── graphbg.jpeg
│   │   ├── homebg4.jpg
│   │   ├── tablebg.jpg
│   │   └── viewbg.jpg
│   └── js/
│       └── script.js
│
├── templates/
│   ├── index.html
│   ├── about.html
│   ├── blog.html
│   ├── cal.html
│   ├── graph.html
│   ├── top3.html
│   └── view.html
│
├── db.py
├── emissions.sql
├── historical_emissions.csv
├── normalized_historical_emissions.csv
├── sources.csv
├── sql_preprocessing.ipynb
├── requirements.txt
└── README.md

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🛠 Tech Stack

🔹 Backend

• Python 3.x
• Flask
• MySQL
• SQL

🔹 Data & Analytics

• Pandas
• NumPy
• CSV-based real-world emissions datasets

🔹 Frontend

• HTML5, CSS3
• JavaScript (ES6)
• Jinja2 Templates

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🚀 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/ZeroTrace.git
cd ZeroTrace

2️⃣ Create Virtual Environment

python -m venv venv
venv\Scripts\activate      # Windows
source venv/bin/activate    # macOS/Linux

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Database

• Create a MySQL database
• Run emissions.sql to create tables
• Import the provided CSV files

5️⃣ Run the Application

python app.py

6️⃣ Open in Browser

http://127.0.0.1:5000

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🎯 Usage

• 📊 View historical carbon emission trends
• 🌍 Compare emissions by country and sector
• 📋 Explore structured emission datasets
• 🧮 Analyze data through interactive visuals

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📊 Data Processing

• Raw emission datasets cleaned using Pandas
• Normalization and preprocessing via SQL & Python
• Stored in MySQL for efficient querying

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🔮 Future Enhancements

• ML-based emission forecasting
• Automated ETL pipelines
• Cloud deployment (AWS / Azure)
• User authentication & dashboards
• Public API for emission data

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👤 Author

Karthick S Developer | AI Enthusiast | Aspiring Data Engineer

📧 h2karthi04@gmail.com 🌐 GitHub: https://github.com/Karthih2 🔗 LinkedIn: https://www.linkedin.com/in/karthick-s-70108128a

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