💫 Data Visualization Learning Repository

A comprehensive repository containing fundamental and advanced data visualization techniques, interactive applications, and practical examples for learning and implementing data visualization solutions.

📋 Table of Contents

• 🎯 Overview
• 🚀 Quick Start
• 📁 Repository Structure
  • 📊 Python_Vis
  • 🌐 Streamlit
    • 📚 Basic Code
    • 🔧 Combined Code
      • 🎵 Audio Processing
      • 🤖 Natural Language Processing (NLP)
      • 👁️ Computer Vision
      • ⛏️ Data Mining
      • 📈 Time Series Analysis
• 💻 Tech Stack
• 🛠️ Installation
• 📖 Usage
• 🤝 Contributing
• 📄 License

🎯 Overview

This repository serves as a comprehensive learning resource for data visualization, featuring:

• Fundamental Concepts: Basic to advanced data visualization techniques
• Interactive Applications: Streamlit-based web applications for various domains
• Practical Examples: Real-world implementations and use cases
• Multi-Domain Coverage: Audio processing, NLP, computer vision, data mining, and time series analysis
• Educational Resources: Exercises, labs, and solutions for hands-on learning

🚀 Quick Start

1. Clone the repository

   git clone https://github.com/username/Data-Visualization.git
   cd Data-Visualization

2. Install dependencies

   pip install -r Streamlit/requirements.txt

3. Run a Streamlit application

   cd Streamlit/basic_code
   streamlit run your_app.py

📁 Repository Structure

📊 Python_Vis

The Python_Vis directory contains fundamental Python-based data visualization resources:

• 📝 Exercise (14 items): Practice exercises for learning data visualization concepts
• ✅ Exercise Sol (16 items): Complete solutions to exercises with detailed explanations
• 🧪 Labs (13 items): Laboratory assignments and practical implementations
• 📈 Matplotlib Examples (41 items): Extensive collection of Matplotlib visualization examples

🌐 Streamlit

The Streamlit directory houses interactive web applications and resources:

📚 Basic Code

• 📋 CheatSheet: Quick reference guides for Streamlit development
• 🔰 basic_code (60 items): Fundamental Streamlit applications and examples
• 🖼️ static: Static assets and resources for applications

🔧 Combined Code

Advanced applications organized by domain:

🎵 Audio Processing

Interactive applications for audio analysis and processing:

• 🎙️ audio_processing: Audio manipulation and analysis tools
• 📝 transcription: Speech-to-text conversion applications

🤖 Natural Language Processing (NLP)

Comprehensive NLP applications and tools:

• 💬 chatbot: Multiple chatbot implementations
  • API_chatbot: API-based chatbot solutions
  • Agents: AI agent-based conversational systems
  • chat_echo: Simple echo chatbot for testing
  • dummy_chat_bot: Basic chatbot template
  • open_source_chatbot: Open-source chatbot implementations
• 🚫 hate_speech_detector: Content moderation and hate speech detection
• 😊 sentiment_analysis: Emotion and sentiment analysis tools
• 📚 text_classification: Document and text classification systems
• 🧹 text_cleaning: Text preprocessing and cleaning utilities

👁️ Computer Vision

Advanced computer vision applications:

• 🖼️ background_remover: Automatic background removal tools
• 🔄 data_augmentation: Image data augmentation techniques
• 📝 image_caption: Automatic image captioning systems
• 🏷️ image_classification: Image recognition and classification
• 🎯 object_detection: Object detection and localization systems

⛏️ Data Mining

Data mining and machine learning applications:

• 📊 classification: Classification algorithms and implementations
• 📈 linear_regression: Linear regression analysis tools

📈 Time Series Analysis

Time series analysis and forecasting:

• 🔮 forecasting: Time series forecasting applications and models

💻 Tech Stack

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• Git

Setup Instructions

1. Clone the repository

   git clone https://github.com/username/Data-Visualization.git
   cd Data-Visualization

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages

   pip install -r Streamlit/requirements.txt

4. Verify installation

   streamlit --version
   python --version

📖 Usage

Running Streamlit Applications

1. Navigate to the desired application directory

   cd Streamlit/combined_code/NLP/chatbot/API_chatbot

2. Run the application

   streamlit run simple_app.py

3. Access the application

   • Open your browser and go to http://localhost:8501

Exploring Python Visualizations

1. Navigate to Python_Vis directory

   cd Python_Vis/Matplotlib\ Examples

2. Run Python scripts

   python example_script.py

Working with Exercises

1. Start with exercises

   cd Python_Vis/Exercise

2. Check solutions

   cd ../Exsercise\ Sol

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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📧 Contact: Amir Jafari
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