I will hand over a large set of flight performance records pulled directly from our aircraft’s FDR. The focus is squarely on three dimensions—fuel efficiency, engine temperature variations, and thrust levels—and how their interplay drives overall fuel burn. I have detailed data that is, (Gaseous Emissions and Smoke, nvPM Emissions including all type of engines) What I need is a full analytical cycle: data cleaning, exploratory analysis, insightful visualisations and, most importantly, clear recommendations I can action to trim fuel costs on future legs. Python with pandas, NumPy, Sci-Kit Learn (or a comparable scientific stack in R or MATLAB) is ideal because the airline’s ops team must be able to rerun the notebooks internally. Deliverables • Reproducible code and notebooks with inline comments • A concise technical report (PDF or Markdown) that explains methodology, key findings, and concrete steps we should take to improve fuel consumption • Optional: an interactive dashboard (Plotly, Power BI, or Tableau) if it speeds up decision-making Acceptance criteria • All code executes on my end without manual fixes • Statistical claims are backed by tests or confidence intervals • Recommendations clearly quantify potential fuel-save percentages If you have previous aviation or engine-performance projects in your portfolio, please mention them so I can gauge fit quickly.