Flight Delays Analysis

About this project

Project Overview

The National Aviation Administration (NAA) of the United States has been facing increasing flight departure delays in the State of New York, impacting airport operations nationwide. Airlines attribute the issue to a lack of runways, while airports point to inefficiencies in the boarding process. Without a resolution, passenger dissatisfaction may lead to reduced government investment and penalties for the NAA.

As an Analytics Consultant, my role was to analyze the 2021 flight performance data to identify key factors contributing to delays, assess passenger satisfaction, and provide data-driven recommendations to optimize flight operations. Additionally, I leveraged Machine Learning predictions to minimize travel disruptions for passengers flying from New York to Florida for a major event in January 2022.

Key Deliverables

• Power BI Dashboard: Developed a comprehensive visualization report to explore flight delays, satisfaction scores, and operational inefficiencies.

• Statistical & Predictive Analysis: Identified patterns in delayed flights and provided insights into factors influencing passenger satisfaction.

• Operational Strategy: Recommended an optimal office placement strategy for improved airport supervision based on location and passenger volume.

• Machine Learning Model Interpretation: Analyzed the predicted probability of flight delays and advised travelers on the most reliable flights with minimal delay risks.

Key Insights & Impact

✅ Average Take-off Delay & Passenger Satisfaction – Analyzed the 2021 flight data to determine average delays and customer satisfaction trends. 

✅ Influencing Factors – Identified the most significant variable affecting passenger satisfaction and set benchmarks for improvement. 

✅ Seasonal Analysis – Recommended seasonal operational changes to maintain satisfaction above 6.1. 

✅ Optimized Supervision Strategy – Proposed location-based vs. volume-based office placements to enhance airport monitoring.

✅ Delay Pattern Forecasting – Predicted upcoming flight delays and identified past high/low delay periods for further investigation. 

✅ Flight Recommendations – Provided passengers with the top 3 least risky flights based on predictive modeling.

By integrating Power BI for visualization, statistical modeling, and machine learning insights, this project provided actionable solutions to improve airport efficiency, enhance passenger experience, and reduce flight delays in New York State.