📌 Overview In the highly competitive airline industry, maximizing profitability is influenced by multiple operational and financial factors. This project aims to develop a Machine Learning (ML) model that accurately predicts Profit (USD) for flights while also providing actionable insights through Power BI dashboards for better decision-making.

🚀 Objectives ✅ Build a robust ML model to predict airline profitability. ✅ Analyze key features such as: Flight Delays Aircraft Utilization Turnaround Time Load Factor Fleet Availability Maintenance Downtime Fuel Efficiency Revenue & Operating Costs ✅ Provide explainability and business insights. ✅ Visualize operational metrics and performance using Power BI.

🧠 Machine Learning Model ✅ Model Used: Ridge Regression 🎯 Performance: R² Score: .762 MAE: 0.0000 RMSE: 0.0000

📜 License This project is licensed under the MIT License.

💡 Acknowledgements Dataset inspired by aviation KPIs. Tools: Python, Power BI, Pandas, Scikit-Learn, Matplotlib, Seaborn.