# 🚛 Advanced US Freight Analytics Dashboard
## 🎯 Overview
An interactive, data-driven dashboard for comprehensive analysis of US freight transportation patterns across rail and port modes. This enhanced visualization platform provides deep insights into seasonal trends, performance metrics, and predictive analytics for freight transportation.
## 🚀 Enhanced Features
### ✨ **Professional UI/UX**
- Modern, responsive design with custom CSS styling
- Interactive metric cards and KPI displays
- Mobile-friendly layouts
### 📊 **Advanced Analytics**
- **Multi-Modal Analysis**: Compare rail and port freight transportation
- **Seasonal Decomposition**: Deep dive into seasonal patterns
- **Trend Analysis**: Statistical trend detection with R-squared values
- **Predictive Insights**: Moving averages and anomaly detection
- **Interactive Heatmaps**: Correlation analysis between variables
### 🗺️ **Enhanced Geospatial Visualization**
- Interactive port location maps with volume bubbles
- Regional analysis by coast (Atlantic, Pacific, Gulf)
- Geographic performance distribution
### 📈 **Advanced Chart Types**
- Sunburst charts for hierarchical data
- Interactive heatmaps with hover details
- Time series with statistical trend lines
- Growth rate analysis with year-over-year comparisons
- Capacity utilization indicators
## 📂 Project Structure
```
DashBoard/
├── 📱 streamlit_app.py
├── 📋 requirements.txt
├── 📖 README.md
├── 📜 LICENSE
├── 🔧 .gitignore
├── 🛠️ setup.py
├── 📝 CHANGELOG.md
├── 🤝 CONTRIBUTING.md
├── 🔐 SECURITY.md
├── 🚀 run_dashboard.bat
├── 📊 Data/
│ ├── Rail_Carloadings_originated.csv
│ └── port_dataset.json
├── 📁 Script/
│ ├── enhanced_dashboard.py
│ ├── dash_water_rail.py
│ └── test_dashboard.py
├── ⚙️ .vscode/
├── 🐳 .devcontainer/
└── 🔄 .github/
├── workflows/
│ └── ci.yml
└── ISSUE_TEMPLATE/
├── bug_report.yml
└── feature_request.yml
```
## 🎨 Dashboard Sections
### 1. **🚆 Rail Analytics**
- **Overview**: Trend analysis with statistical insights, interactive heatmaps
- **Seasonal Analysis**: Sunburst charts, year-over-year seasonal comparisons
- **Trend Analysis**: Growth rate calculations, performance tracking
- **Predictive Insights**: Moving averages, anomaly detection
### 2. **🚢 Port Analytics**
- **Overview**: Interactive maps, time series comparisons
- **Performance Metrics**: Capacity utilization, regional analysis
- **Seasonal Patterns**: Coast-based seasonal analysis
- **Growth Analysis**: Port ranking and performance trends
### 3. **📊 Comparative Analysis**
- **Multi-Modal Comparison**: Rail vs Port volume analysis
- **Market Share Evolution**: Modal share tracking over time
- **Conversion Analytics**: TEU equivalent calculations
- **Strategic Insights**: Mode-specific advantages analysis
## 🛠️ Technical Features
### **Performance Optimizations**
- `@st.cache_data` for efficient data loading
- Progressive loading for large datasets
### **Advanced Libraries**
- **Plotly**: Interactive charts with hover details
- **SciPy**: Statistical analysis and trend detection
- **Pandas**
- **NumPy**
## 🚀 Quick Start
### 📦 **Package Installation (Recommended)**
```bash
# Install from PyPI (when published)
pip install freight-analytics-dashboard
# Launch dashboard immediately
freight-dashboard
# Custom configuration
freight-dashboard --port 8502 --host 0.0.0.0
# Get help
freight-dashboard --help
```
### 🌐 **Live Demo**
**[View Live Dashboard on Streamlit Cloud](https://meghkc-dashboard-freight-analysis.streamlit.app/)** 🔗
### 💻 **Local Development**
#### **Option 1: From Package Source**
```bash
# Clone the repository
git clone https://github.com/meghkc/DashBoard.git
cd DashBoard
# Install in development mode
pip install -e .
# Launch via CLI
freight-dashboard
```
#### **Option 2: Direct Streamlit**
```bash
# Clone and navigate
git clone https://github.com/meghkc/DashBoard.git
cd DashBoard
# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run the main dashboard (Streamlit Cloud compatible)
streamlit run streamlit_app.py
```
#### **Option 3: One-Click Launch (Windows)**
```bash
# Double-click the launcher
run_dashboard.bat
```
### 🐳 **Container Deployment**
```bash
# Docker
docker build -t freight-dashboard .
docker run -p 8501:8501 freight-dashboard
# Or use pre-built image (when available)
docker run -p 8501:8501 meghkc/freight-analytics-dashboard
```
### ☁️ **Cloud Deployment**
- **Streamlit Cloud**: Fork repo → Connect GitHub → Deploy
- **Heroku/Railway/Render**: Direct deployment support via `Procfile`
- **Any Python hosting**: Install package and run `freight-dashboard`
```bash
## 📊 Data Sources & Specifications
### **Rail Dataset**
- **Source**: USDA Agricultural Transportation
- **Timespan**: 2017-2023 (7 years)
- **Key Metrics**: Carloads by railroad, commodity, and time
### **Port Dataset**
- **Source**: Individual port authority websites
- **Coverage**: 9 major US container ports
- **Timespan**: 2018-2024
- **Key Metrics**: TEU (Twenty-foot Equivalent Units)
## 🎯 Analytics Features
### **KPI Dashboard**
- Total freight volume metrics
- Growth rate calculations
- Peak performance indicators
- Operational efficiency metrics
### **Data Insights**
- Anomaly detection alerts
- Seasonal pattern recognition
- Performance benchmarking
- Trend significance testing
### **Export Capabilities**
- Data download options
- Chart export functionality
- Report generation ready
## 👨💻 Technical Specifications
### **System Requirements**
- Python 3.8+
- 4GB RAM minimum
- Modern web browser
- Internet connection for maps
### **Dependencies**
streamlit >= 1.48.0
pandas >= 1.5.0
plotly >= 5.0.0
scipy >= 1.9.0
scikit-learn >= 1.1.0
seaborn >= 0.11.0
numpy >= 1.21.0
## 🔗 Links & Resources
- **Data Source (Rail)**: [USDA Agricultural Transportation](https://agtransport.usda.gov/stories/s/appm-bhti)
- **Data Source (Ports)**: Individual port authority websites
- **Framework**: Built with Streamlit
- **Visualization**: Powered by Plotly
- **Author**: Megh KC | Created 2024 | Enhanced 2025
---
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"description": "# \ud83d\ude9b Advanced US Freight Analytics Dashboard\r\n\r\n## \ud83c\udfaf Overview\r\nAn interactive, data-driven dashboard for comprehensive analysis of US freight transportation patterns across rail and port modes. This enhanced visualization platform provides deep insights into seasonal trends, performance metrics, and predictive analytics for freight transportation.\r\n\r\n## \ud83d\ude80 Enhanced Features\r\n\r\n### \u2728 **Professional UI/UX**\r\n- Modern, responsive design with custom CSS styling\r\n- Interactive metric cards and KPI displays\r\n- Mobile-friendly layouts\r\n\r\n### \ud83d\udcca **Advanced Analytics**\r\n- **Multi-Modal Analysis**: Compare rail and port freight transportation\r\n- **Seasonal Decomposition**: Deep dive into seasonal patterns\r\n- **Trend Analysis**: Statistical trend detection with R-squared values\r\n- **Predictive Insights**: Moving averages and anomaly detection\r\n- **Interactive Heatmaps**: Correlation analysis between variables\r\n\r\n### \ud83d\uddfa\ufe0f **Enhanced Geospatial Visualization**\r\n- Interactive port location maps with volume bubbles\r\n- Regional analysis by coast (Atlantic, Pacific, Gulf)\r\n- Geographic performance distribution\r\n\r\n### \ud83d\udcc8 **Advanced Chart Types**\r\n- Sunburst charts for hierarchical data\r\n- Interactive heatmaps with hover details\r\n- Time series with statistical trend lines\r\n- Growth rate analysis with year-over-year comparisons\r\n- Capacity utilization indicators\r\n\r\n## \ud83d\udcc2 Project Structure\r\n\r\n```\r\nDashBoard/\r\n\u251c\u2500\u2500 \ud83d\udcf1 streamlit_app.py \r\n\u251c\u2500\u2500 \ud83d\udccb requirements.txt \r\n\u251c\u2500\u2500 \ud83d\udcd6 README.md \r\n\u251c\u2500\u2500 \ud83d\udcdc LICENSE \r\n\u251c\u2500\u2500 \ud83d\udd27 .gitignore \r\n\u251c\u2500\u2500 \ud83d\udee0\ufe0f setup.py \r\n\u251c\u2500\u2500 \ud83d\udcdd CHANGELOG.md \r\n\u251c\u2500\u2500 \ud83e\udd1d CONTRIBUTING.md \r\n\u251c\u2500\u2500 \ud83d\udd10 SECURITY.md \r\n\u251c\u2500\u2500 \ud83d\ude80 run_dashboard.bat \r\n\u251c\u2500\u2500 \ud83d\udcca Data/ \r\n\u2502 \u251c\u2500\u2500 Rail_Carloadings_originated.csv \r\n\u2502 \u2514\u2500\u2500 port_dataset.json \r\n\u251c\u2500\u2500 \ud83d\udcc1 Script/ \r\n\u2502 \u251c\u2500\u2500 enhanced_dashboard.py \r\n\u2502 \u251c\u2500\u2500 dash_water_rail.py \r\n\u2502 \u2514\u2500\u2500 test_dashboard.py \r\n\u251c\u2500\u2500 \u2699\ufe0f .vscode/ \r\n\u251c\u2500\u2500 \ud83d\udc33 .devcontainer/ \r\n\u2514\u2500\u2500 \ud83d\udd04 .github/ \r\n \u251c\u2500\u2500 workflows/\r\n \u2502 \u2514\u2500\u2500 ci.yml \r\n \u2514\u2500\u2500 ISSUE_TEMPLATE/\r\n \u251c\u2500\u2500 bug_report.yml \r\n \u2514\u2500\u2500 feature_request.yml \r\n```\r\n\r\n## \ud83c\udfa8 Dashboard Sections\r\n\r\n### 1. **\ud83d\ude86 Rail Analytics**\r\n- **Overview**: Trend analysis with statistical insights, interactive heatmaps\r\n- **Seasonal Analysis**: Sunburst charts, year-over-year seasonal comparisons\r\n- **Trend Analysis**: Growth rate calculations, performance tracking\r\n- **Predictive Insights**: Moving averages, anomaly detection\r\n\r\n### 2. **\ud83d\udea2 Port Analytics**\r\n- **Overview**: Interactive maps, time series comparisons\r\n- **Performance Metrics**: Capacity utilization, regional analysis\r\n- **Seasonal Patterns**: Coast-based seasonal analysis\r\n- **Growth Analysis**: Port ranking and performance trends\r\n\r\n### 3. **\ud83d\udcca Comparative Analysis**\r\n- **Multi-Modal Comparison**: Rail vs Port volume analysis\r\n- **Market Share Evolution**: Modal share tracking over time\r\n- **Conversion Analytics**: TEU equivalent calculations\r\n- **Strategic Insights**: Mode-specific advantages analysis\r\n\r\n## \ud83d\udee0\ufe0f Technical Features\r\n\r\n### **Performance Optimizations**\r\n- `@st.cache_data` for efficient data loading\r\n- Progressive loading for large datasets\r\n\r\n### **Advanced Libraries**\r\n- **Plotly**: Interactive charts with hover details\r\n- **SciPy**: Statistical analysis and trend detection\r\n- **Pandas**\r\n- **NumPy**\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### \ud83d\udce6 **Package Installation (Recommended)**\r\n```bash\r\n# Install from PyPI (when published)\r\npip install freight-analytics-dashboard\r\n\r\n# Launch dashboard immediately \r\nfreight-dashboard\r\n\r\n# Custom configuration\r\nfreight-dashboard --port 8502 --host 0.0.0.0\r\n\r\n# Get help\r\nfreight-dashboard --help\r\n```\r\n\r\n### \ud83c\udf10 **Live Demo**\r\n**[View Live Dashboard on Streamlit Cloud](https://meghkc-dashboard-freight-analysis.streamlit.app/)** \ud83d\udd17\r\n\r\n### \ud83d\udcbb **Local Development**\r\n\r\n#### **Option 1: From Package Source**\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/meghkc/DashBoard.git\r\ncd DashBoard\r\n\r\n# Install in development mode\r\npip install -e .\r\n\r\n# Launch via CLI\r\nfreight-dashboard\r\n```\r\n\r\n#### **Option 2: Direct Streamlit**\r\n```bash\r\n# Clone and navigate\r\ngit clone https://github.com/meghkc/DashBoard.git\r\ncd DashBoard\r\n\r\n# Create virtual environment (recommended)\r\npython -m venv venv\r\nsource venv/bin/activate # Windows: venv\\Scripts\\activate\r\n\r\n# Install dependencies\r\npip install -r requirements.txt\r\n\r\n# Run the main dashboard (Streamlit Cloud compatible)\r\nstreamlit run streamlit_app.py\r\n```\r\n\r\n#### **Option 3: One-Click Launch (Windows)**\r\n```bash\r\n# Double-click the launcher\r\nrun_dashboard.bat\r\n```\r\n\r\n### \ud83d\udc33 **Container Deployment**\r\n```bash\r\n# Docker\r\ndocker build -t freight-dashboard .\r\ndocker run -p 8501:8501 freight-dashboard\r\n\r\n# Or use pre-built image (when available)\r\ndocker run -p 8501:8501 meghkc/freight-analytics-dashboard\r\n```\r\n\r\n### \u2601\ufe0f **Cloud Deployment**\r\n- **Streamlit Cloud**: Fork repo \u2192 Connect GitHub \u2192 Deploy\r\n- **Heroku/Railway/Render**: Direct deployment support via `Procfile`\r\n- **Any Python hosting**: Install package and run `freight-dashboard`\r\n```bash\r\n## \ud83d\udcca Data Sources & Specifications\r\n\r\n### **Rail Dataset**\r\n- **Source**: USDA Agricultural Transportation\r\n- **Timespan**: 2017-2023 (7 years)\r\n- **Key Metrics**: Carloads by railroad, commodity, and time\r\n\r\n### **Port Dataset**\r\n- **Source**: Individual port authority websites\r\n- **Coverage**: 9 major US container ports\r\n- **Timespan**: 2018-2024\r\n- **Key Metrics**: TEU (Twenty-foot Equivalent Units)\r\n\r\n## \ud83c\udfaf Analytics Features\r\n\r\n### **KPI Dashboard**\r\n- Total freight volume metrics\r\n- Growth rate calculations\r\n- Peak performance indicators\r\n- Operational efficiency metrics\r\n\r\n### **Data Insights**\r\n- Anomaly detection alerts\r\n- Seasonal pattern recognition\r\n- Performance benchmarking\r\n- Trend significance testing\r\n\r\n### **Export Capabilities**\r\n- Data download options\r\n- Chart export functionality\r\n- Report generation ready\r\n\r\n## \ud83d\udc68\u200d\ud83d\udcbb Technical Specifications\r\n\r\n### **System Requirements**\r\n- Python 3.8+\r\n- 4GB RAM minimum\r\n- Modern web browser\r\n- Internet connection for maps\r\n\r\n### **Dependencies**\r\n\r\nstreamlit >= 1.48.0\r\npandas >= 1.5.0\r\nplotly >= 5.0.0\r\nscipy >= 1.9.0\r\nscikit-learn >= 1.1.0\r\nseaborn >= 0.11.0\r\nnumpy >= 1.21.0\r\n\r\n## \ud83d\udd17 Links & Resources\r\n\r\n- **Data Source (Rail)**: [USDA Agricultural Transportation](https://agtransport.usda.gov/stories/s/appm-bhti)\r\n- **Data Source (Ports)**: Individual port authority websites\r\n- **Framework**: Built with Streamlit\r\n- **Visualization**: Powered by Plotly\r\n- **Author**: Megh KC | Created 2024 | Enhanced 2025\r\n\r\n---\r\n",
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