Modern Data Visualization App

Overview

The Modern Data Visualization App is a Python-based application built using CustomTkinter for the graphical user interface (GUI) and Matplotlib for data visualization. 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.

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Features

1. Data Import:

   • Import datasets in CSV format.
   • Automatically detect numerical and categorical columns.

2. Data Visualization:

   • Supports multiple chart types:
     • Line Graph
     • Bar Chart
     • Pie Chart
     • Scatter Plot
     • Histogram
     • Area Chart
   • Dynamically update visualizations based on user input.

3. Real-Time Data Streaming:

   • Simulate real-time data updates for dynamic visualization.

4. Machine Learning Integration:

   • Apply machine learning techniques:
     • Trend Line (Linear Regression)
     • Clustering (K-Means)

5. Overall Calculations:

   • Display summary statistics for numerical columns (mean, median, min, max, etc.).
   • Display value counts for categorical columns.

6. Export Graphs:

   • Save visualizations as PNG, JPEG, or PDF files.

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How to Use

1. Import Data:

   • Click the Import Data button to load a CSV file.
   • The app will automatically detect and display the columns in the dataset.

2. Select Column and Chart Type:

   • Choose a column from the dropdown menu.
   • Select a chart type (e.g., Line Graph, Bar Chart, Pie Chart).

3. Update Visualization:

   • Click the Update Visualization button to generate the graph.

4. Enable Real-Time Streaming:

   • Check the Enable Real-Time Streaming checkbox to simulate real-time data updates.

5. Apply Machine Learning:

   • Select a machine learning option (Trend Line or Clustering) from the dropdown menu.

6. Export Graphs:

   • Click the Export Graph button to save the current visualization as an image.

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Prerequisites

To run the app, ensure you have the following Python libraries installed:

• customtkinter
• matplotlib
• pandas
• numpy
• scikit-learn

You can install the required libraries using pip:

pip install customtkinter matplotlib pandas numpy scikit-learn

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Code Structure

• GUI:

  • Built using CustomTkinter for a modern and customizable interface.
  • Includes a sidebar for controls and a main content area for visualizations.

• Data Handling:

  • Uses pandas to read and process CSV files.
  • Automatically detects numerical and categorical columns.

• Visualization:

  • Uses matplotlib to create and display graphs.
  • Supports multiple chart types and dynamic updates.

• Machine Learning:

  • Integrates scikit-learn for trend line fitting and clustering.

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Example Dataset

The app works with any CSV dataset. Here’s an example dataset (students.csv):

Name,Age,Math Score,Science Score,English Score,Grade
Alice,18,85,90,88,A
Bob,17,78,82,75,B
Charlie,19,92,88,91,A
Diana,18,65,70,68,C
Eva,17,88,85,90,A
Frank,19,72,75,70,B
Grace,18,95,92,94,A
Henry,17,60,65,62,D
Ivy,19,80,78,82,B
Jack,18,55,50,58,F

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Explanation

The 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:

• Dynamic Updates: Visualizations update in real-time based on user input.
• Flexibility: Supports both numerical and categorical data.
• Extensibility: Easy to add new features and enhancements.

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How to Run

1. Clone the repository or download the script.
2. Install the required libraries (see Prerequisites).
3. Run the script:

   python app.py

4. Use the app to import data, visualize it, and perform analysis.

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License

This project is open-source and available under the MIT License. Feel free to use, modify, and distribute it as needed.

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Author

• Hariom Kumar
• Contact: [Your Email Address]

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Enjoy exploring your data with the Modern Data Visualization App! Let me know if you have any questions or suggestions. 🚀