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ProAOG: AI-Powered Predictive Fleet Maintenance Platform proAog_log_resize_28

#Hello, I'm SYED AMJAD 👋 I am a Strategic Business & Data Analyst and a specialist in Artificial Intelligence & Data Science. My focus is on leveraging predictive modeling and complex data engineering to drive critical operational efficiency and strategic business outcomes, particularly within high-stakes environments like Airlines Engineering and Supply chain Operations. 🌟 Featured Project: ProAOG Predictive System ProAOG is a critical, high-impact predictive modeling application designed to minimize operational downtime and associated costs for airlines. This is a self-initiated capstone project leveraging deep domain expertise in aviation engineering to solve the industry's most costly problem: Aircraft On Ground (AOG) events. 🌟Category Detail *Business Impact Reduces critical aircraft delays by 15% (target) by enabling preemptive resource and personnel deployment. *Core Integration Generates high-confidence alerts that integrate directly with Passenger & Flight Operations for scheduling adjustments, and feeds MRO recommendations to Maintenance & Engineering teams. *Technology Stack Python (Scikit-learn, Pandas, Streamlit), Predictive Algorithms, SQL, Git/GitHub.

*Model Architecture Designing a modular LSTM (Long Short-Term Memory) architecture to process complex, high-frequency time-series sensor data. *Optimization Target Achieve a reliable 72-hour prediction window for component failure.

*Focus Systems Predicting degradation within modern fleet components: Boeing 787 /777APU and Airbus A380 /A350 EHA Actuators.

🚀 Key Operational Features The ProAOG platform is designed to seamlessly integrate data intelligence into critical aviation workflows: • AOG Forecasting (Predictive Alerts): Provides a 72-hour lead time on high-risk component degradation using time-series LSTM models, enabling proactive intervention before failure occurs. • Passenger & Flight Operations Integration: High-confidence failure alerts are pushed directly to scheduling teams to preemptively adjust flight assignments, minimizing passenger disruption and logistical chaos. • ⚙️ Maintenance & Engineering Workflow: Automatically generates MRO (Maintenance, Repair, and Overhaul) recommendations, optimizing labor scheduling, inventory management, and technician deployment. 🛠️ Core Skills • Data Analysis & ML: Python, Pandas, Scikit-learn, Statistical Modeling, Power BI, Tableau, LSTM Networks. • Business Strategy: Digital Transformation, Business Process Re-engineering, Six Sigma, Requirements Elicitation. • Databases & Tools: SQL, Firestore, Streamlit, Git/GitHub. 📫 Let's Connect • LinkedIn: syed-amjad-9b513570 • WhatsApp (Direct Chat): 00923352177766 • Project Documentation: [Link to your dedicated ProAOG project documentation/whitepaper]


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ProAOG — Aviation Data Science Capstone: LSTM modeling for B787/A350 component failure prediction.

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