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📖 About The Project

No code. No hassle. Just load your data and visualize it instantly.

Data Analytics Dashboard is a desktop GUI application that transforms raw CSV data into beautiful, meaningful charts — all without writing a single line of code. Designed for students, analysts, and data enthusiasts who want fast, visual insights.

Whether you're exploring sales trends, understanding distributions, or identifying correlations — this dashboard has you covered with 10 powerful chart types and an intuitive point-and-click interface.

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✨ Features

🎯 Core Features 📁 One-Click CSV Loading — drag, select & preview instantly 📊 10 Chart Types — from pie to pairplot 🔍 Data Preview Window — see columns and rows before plotting 🎛️ Smart Column Selector — dropdown-based axis selection 💾 Export Charts — save your plots as PNG/PDF

🧪 Visualization Types # Chart Use Case 1 🥧 Pie Chart Proportions 2 📈 Line Plot Trends over time 3 🔵 Scatter Plot Correlations 4 📊 Histogram Distributions 5 📋 Bar Chart Comparisons 6 📦 Box Plot Outlier detection 7 🎻 Violin Plot Data spread 8 🌡️ Heatmap Correlations 9 🔗 Pair Plot Multi-variable 10 🔢 Count Plot Frequencies

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🛠️ Built With

Technology	Version	Role
	3.7+	Core application logic
	Built-in	Graphical user interface
	Latest	Data loading & manipulation
	Latest	Statistical visualizations
	Latest	Rendering & figure control

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🚀 Getting Started

Prerequisites

Ensure Python 3.7+ is installed on your system:

python --version

Installation

1. Clone the repository

git clone https://github.com/SOMU3103/Data_Analytics.git

2. Navigate into the project folder

cd Data_Analytics

3. Install required libraries

pip install pandas seaborn matplotlib

💡 Tkinter comes bundled with Python by default. If missing: sudo apt-get install python3-tk (Linux)

4. Run the application

python Data_Analytice_Dashboard.py

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🎮 How To Use

1. Launch the app
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2. Click "📁 Load CSV File"
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3. Preview your data (columns + rows shown automatically)
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4. Select X-axis and Y-axis columns from dropdowns
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5. Click any chart button (Pie, Line, Bar, etc.)
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6. Your chart appears instantly! Save or export as needed.

📄 Sample CSV to Try

Month,Sales,Profit,Region
January,15000,3000,North
February,18000,4200,South
March,12000,2800,East
April,21000,5100,West
May,19500,4700,North
June,23000,6000,South

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🤝 Contributing

Contributions make open-source awesome! Here's how to get involved:

# Step 1 — Fork the repo on GitHub

# Step 2 — Clone your fork
git clone https://github.com/YOUR_USERNAME/Data_Analytics.git

# Step 3 — Create a feature branch
git checkout -b feature/YourFeatureName

# Step 4 — Make your changes, then commit
git commit -m "✨ Add: YourFeatureName"

# Step 5 — Push and open a Pull Request
git push origin feature/YourFeatureName

💡 Ideas for Contributions

• 📂 Add Excel/JSON file support
• 🌙 Dark mode theme
• 🧹 Built-in data cleaning tools
• 📤 Export to HTML report
• 📐 Resize & customize chart dimensions
• 🧠 Summary statistics panel

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📝 License

Distributed under the MIT License. See LICENSE for details.

MIT License — Copyright (c) 2026 Somnath (SOMU3103)
Free to use, modify, and distribute with attribution.

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📬 Contact

👨‍💻 Somnath

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🙏 Acknowledgments

• 🐍 Python Community — for the amazing ecosystem
• 🐼 Pandas Team — for effortless data manipulation
• 🎨 Seaborn Team — for gorgeous statistical visuals
• 📊 Matplotlib Team — for the visualization backbone
• 💙 Everyone who stars ⭐ this repository

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If this project helped you, consider giving it a ⭐ — it means a lot!

Made with ❤️ by Somnath