{"id":24961338,"url":"https://github.com/hari7261/data-visualization","last_synced_at":"2025-06-17T23:03:56.825Z","repository":{"id":272457316,"uuid":"916654392","full_name":"hari7261/Data-Visualization","owner":"hari7261","description":"Python-based application built using CustomTkinter for the graphical user interface (GUI) and Matplotlib for data visualization. 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It allows users to import datasets, perform real-time data visualization, and analyze data using various chart types and machine learning techniques. The app is designed to be user-friendly and flexible, supporting both numerical and categorical data.\r\n\r\n---\r\n\r\n## Features\r\n1. **Data Import**:\r\n   - Import datasets in CSV format.\r\n   - Automatically detect numerical and categorical columns.\r\n\r\n2. **Data Visualization**:\r\n   - Supports multiple chart types:\r\n     - Line Graph\r\n     - Bar Chart\r\n     - Pie Chart\r\n     - Scatter Plot\r\n     - Histogram\r\n     - Area Chart\r\n   - Dynamically update visualizations based on user input.\r\n\r\n3. **Real-Time Data Streaming**:\r\n   - Simulate real-time data updates for dynamic visualization.\r\n\r\n4. **Machine Learning Integration**:\r\n   - Apply machine learning techniques:\r\n     - Trend Line (Linear Regression)\r\n     - Clustering (K-Means)\r\n\r\n5. **Overall Calculations**:\r\n   - Display summary statistics for numerical columns (mean, median, min, max, etc.).\r\n   - Display value counts for categorical columns.\r\n\r\n6. **Export Graphs**:\r\n   - Save visualizations as PNG, JPEG, or PDF files.\r\n\r\n---\r\n\r\n## How to Use\r\n1. **Import Data**:\r\n   - Click the **Import Data** button to load a CSV file.\r\n   - The app will automatically detect and display the columns in the dataset.\r\n\r\n2. **Select Column and Chart Type**:\r\n   - Choose a column from the dropdown menu.\r\n   - Select a chart type (e.g., Line Graph, Bar Chart, Pie Chart).\r\n\r\n3. **Update Visualization**:\r\n   - Click the **Update Visualization** button to generate the graph.\r\n\r\n4. **Enable Real-Time Streaming**:\r\n   - Check the **Enable Real-Time Streaming** checkbox to simulate real-time data updates.\r\n\r\n5. **Apply Machine Learning**:\r\n   - Select a machine learning option (Trend Line or Clustering) from the dropdown menu.\r\n\r\n6. **Export Graphs**:\r\n   - Click the **Export Graph** button to save the current visualization as an image.\r\n\r\n---\r\n\r\n## Prerequisites\r\nTo run the app, ensure you have the following Python libraries installed:\r\n- `customtkinter`\r\n- `matplotlib`\r\n- `pandas`\r\n- `numpy`\r\n- `scikit-learn`\r\n\r\nYou can install the required libraries using pip:\r\n```bash\r\npip install customtkinter matplotlib pandas numpy scikit-learn\r\n```\r\n\r\n---\r\n\r\n## Code Structure\r\n- **GUI**:\r\n  - Built using `CustomTkinter` for a modern and customizable interface.\r\n  - Includes a sidebar for controls and a main content area for visualizations.\r\n\r\n- **Data Handling**:\r\n  - Uses `pandas` to read and process CSV files.\r\n  - Automatically detects numerical and categorical columns.\r\n\r\n- **Visualization**:\r\n  - Uses `matplotlib` to create and display graphs.\r\n  - Supports multiple chart types and dynamic updates.\r\n\r\n- **Machine Learning**:\r\n  - Integrates `scikit-learn` for trend line fitting and clustering.\r\n\r\n---\r\n\r\n## Example Dataset\r\nThe app works with any CSV dataset. Here’s an example dataset (`students.csv`):\r\n\r\n```csv\r\nName,Age,Math Score,Science Score,English Score,Grade\r\nAlice,18,85,90,88,A\r\nBob,17,78,82,75,B\r\nCharlie,19,92,88,91,A\r\nDiana,18,65,70,68,C\r\nEva,17,88,85,90,A\r\nFrank,19,72,75,70,B\r\nGrace,18,95,92,94,A\r\nHenry,17,60,65,62,D\r\nIvy,19,80,78,82,B\r\nJack,18,55,50,58,F\r\n```\r\n\r\n---\r\n\r\n\r\n\r\n## Explanation\r\nThe app is designed to be a versatile tool for data visualization and analysis. It combines the simplicity of a GUI with the power of Python's data science libraries. Key features include:\r\n- **Dynamic Updates**: Visualizations update in real-time based on user input.\r\n- **Flexibility**: Supports both numerical and categorical data.\r\n- **Extensibility**: Easy to add new features and enhancements.\r\n\r\n---\r\n\r\n## How to Run\r\n1. Clone the repository or download the script.\r\n2. Install the required libraries (see **Prerequisites**).\r\n3. Run the script:\r\n   ```bash\r\n   python app.py\r\n   ```\r\n4. Use the app to import data, visualize it, and perform analysis.\r\n\r\n---\r\n\r\n## License\r\nThis project is open-source and available under the MIT License. Feel free to use, modify, and distribute it as needed.\r\n\r\n---\r\n\r\n## Author\r\n- **Hariom Kumar**\r\n- Contact: [Your Email Address]\r\n\r\n---\r\n\r\nEnjoy exploring your data with the **Modern Data Visualization App**! Let me know if you have any questions or suggestions. 🚀","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhari7261%2Fdata-visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhari7261%2Fdata-visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhari7261%2Fdata-visualization/lists"}