Moonquake Classification Dashboard

A comprehensive Shiny dashboard for analyzing and classifying moonquake data using machine learning models.

🌟 Features

• Interactive Data Visualization

  • Time series plots of various seismic features
  • Feature analysis with scatter plots
  • Seismogram and spectrogram visualization
  • Data distribution analysis (histogram and pie chart)

• Machine Learning Results

  • CatBoost model performance metrics
  • Random Forest classification results
  • ANN (H2O) model analysis
  • Confusion matrices for all models
  • Feature importance visualization

• User Interface

  • Modern landing page with animations
  • Intuitive navigation
  • Interactive plots with zoom and hover capabilities
  • Responsive design

📊 Data Analysis

The dashboard provides insights into:

• Moonquake type distribution
• Feature importance for classification
• Model performance comparison
• Raw seismic data visualization

🛠️ Technical Stack

• R Packages
  • shiny
  • tidyverse
  • plotly
  • DT
  • signal
  • viridis
  • shinyjs

🚀 Getting Started

1. Prerequisites

   • R (version 4.0 or higher)
   • Required R packages (install using install.packages())

2. Installation

   # Install required packages
   install.packages(c("shiny", "tidyverse", "plotly", "DT", "signal", "viridis", "shinyjs"))

3. Running the App

   # Clone the repository
   git clone https://github.com/ArneshBanerjee/Moonquake-Classification.git
   
   # Navigate to the project directory
   cd Moonquake-Classification
   
   # Run the Shiny app
   R -e "shiny::runApp('app.R')"

📈 Model Performance

CatBoost

• Accuracy: 73.33%
• Precision (impact_mq): 85%
• Recall (impact_mq): 85%
• F1-score (impact_mq): 85%

Random Forest

• Accuracy: 93.33%
• Precision (impact_mq): 93.33%
• Recall (impact_mq): 100%
• F1-score (impact_mq): 96.55%

ANN (H2O)

• Accuracy: 14.29%
• Precision (impact_mq): 85.71%
• Recall (impact_mq): 100%
• F1-score (impact_mq): 92.31%

👥 Team Members

• Arnesh Banerjee
• Ayushi Bhattachrjee

📝 License

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

🙏 Acknowledgments

• NASA Space Apps Challenge 2024
• All contributors and team members
• Open-source community for their valuable tools and libraries