{"id":26017011,"url":"https://github.com/asayda01/airport_operations_analytics","last_synced_at":"2026-05-03T18:32:31.833Z","repository":{"id":267528079,"uuid":"901536719","full_name":"asayda01/Airport_Operations_Analytics","owner":"asayda01","description":"The project analyzes the performance of Hold Baggage Screening X-ray Machines, identifying trends in system load, bottlenecks, and operator efficiency. 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The analysis is based on bag throughput data for the month of **July**, focusing on identifying **performance challenges, bottlenecks, and opportunities for optimization**.\n\n### Deployed App\nThe analysis is available as an interactive **Streamlit app**, accessible at:\n\n🔗 [Airport_Operations_Analytics](https://cem-saydam-airport-operations-analytics.streamlit.app/)\n\n🔗 [https://cem-saydam-airport-operations-analytics.streamlit.app/](https://cem-saydam-airport-operations-analytics.streamlit.app/)\n---\n## Dataset Description\nThe dataset used for this analysis includes the following key attributes:\n\n- **bag_scan_timestamp**: Date and time a bag was seen at a machine.\n- **bag_license_plate**: Unique identifier for a bag.\n- **scan_machine_id**: Unique identifier for a machine.\n- **scan_machine_cluster**: The cluster each machine belongs to.\n- **scan_machine_level**: The current screening level the bag is being processed at.\n- **scan_machine_result**: The screening result of the bag at its current screening level.\n- **scan_machine_result_reason**: Further detail on the screening result of the bag.\n\nThe analysis addresses several key questions, including:\n\n✅ **Throughput and load distribution**\n\n✅ **Peak days and times for bag screening**\n\n✅ **System bottlenecks and time-out situations**\n\n✅ **Machine and cluster utilization**\n\n✅ **Screening escalations and Level 2 analysis**\n\n✅ **Single vs. multiple screenings**\n\n✅ **Decision-making times**\n\n✅ **Operator interventions**\n\n---\n## Repository Structure\n\n```\n├── Cem_Saydam_Streamlit.py       # Main Streamlit app script\n├── Xray_Scan_Data_Jul_2022.csv    # Dataset used for analysis\n├── company_logo.JPG               # Company logo used in the app\n├── README.md                      # This file\n└── requirements.txt               # Dependencies required to run the app\n```\n\n---\n## How to Run the App Locally\nFollow these steps to run the analysis locally:\n\n### 1️⃣ Clone the repository:\n```sh\ngit clone https://github.com/asayda01/Airport_Operations_Analytics\ncd Airport_Operations_Analytics\n```\n\n### 2️⃣ Install dependencies:\n```sh\npip install -r requirements.txt\n```\n\n### 3️⃣ Run the Streamlit app:\n```sh\nstreamlit run streamlit_app.py\n```\n\n### 4️⃣ Access the app:\nOpen your web browser and navigate to:\n[http://localhost:8501](http://localhost:8501)\n\n---\n## Key Insights\n### **1. Throughput and Load Distribution**\n- **Daily Throughput**: Identifies peak days and overall daily bag processing capacity.\n- **Hourly Throughput**: Highlights the busiest hours for screening.\n- **15-Minute Intervals**: Detects short-term spikes in activity.\n\n### **2. Peak Days of Week Analysis**\n- Determines the busiest days and suggests operator schedule optimization.\n\n### **3. System Bottlenecks and Time-Outs**\n- Assesses machine clusters prone to time-outs.\n- Identifies peak times for system slowdowns.\n\n### **4. Machine and Cluster Utilization**\n- Evaluates equitable distribution of bag processing.\n- Identifies machines handling excessive loads and investigates malfunctions.\n\n### **5. Screening Escalations and Level 2 Analysis**\n- Analyzes escalation rates to Level 2 screening.\n- Detects trends in escalation over time and across machines.\n\n### **6. Single vs. Multiple Screenings**\n- Investigates unnecessary bag recirculations.\n- Examines causes of redundant screenings.\n\n### **7. Decision-Making Times**\n- Measures the average time operators spend on screening decisions.\n- Analyzes time intervals between consecutive bag scans.\n\n### **8. Operator Interventions**\n- Identifies the percentage of bags requiring manual review.\n- Examines intervention trends by machine and time of day.\n\n---\n## **Visualizations**\nThe app includes interactive visualizations such as:\n\n📊 **Bar Charts** - Throughput by day, hour, and 15-minute intervals.\n\n📈 **Line Charts** - Time-based trends in Level 2 escalations and recirculations.\n\n🥧 **Pie Charts** - Distribution of screening results and operator interventions.\n\n📦 **Box Plots** - Time spent per bag at each machine.\n\n🔥 **Heatmaps** - Machine and cluster performance visualization.\n\n---\n## **Recommendations**\nBased on the analysis, we provide actionable insights to optimize the screening process:\n\n✅ **Optimizing Staffing** - Align operator schedules with peak screening times.\n\n✅ **Improving Machine Calibration** - Reduce false positives and time-outs.\n\n✅ **Enhancing Maintenance** - Prioritize machines with higher malfunction rates.\n\n✅ **Training Operators** - Reduce intervention times and increase efficiency.\n\n---\n## **Contributing**\nContributions are welcome! If you have suggestions or improvements, please **open an issue** or **submit a pull request**.\n\n---\n## **License**\nThis project is licensed under the **MIT License**. See the `LICENSE` file for details.\n\n---\n## **Acknowledgments**\n- **[Company Name Removed]** - For providing the dataset and supporting this analysis.\n- **Streamlit** - For the framework enabling interactive data exploration.\n\n---\n\n🔗 [Airport_Operations_Analytics](https://cem-saydam-airport-operations-analytics.streamlit.app/)\n\n🚀 **Thank you for visiting! Feel free to explore, contribute, and improve the analysis!**\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasayda01%2Fairport_operations_analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fasayda01%2Fairport_operations_analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasayda01%2Fairport_operations_analytics/lists"}