avihayamor/flight_delay_analysis

U.S. Flight Delay Dataset — 2024

A hypothesis-driven exploratory analysis of 100,000 U.S. domestic flights from 2024, examining what actually drives arrival delays across airlines, airports, and times of day.

Methodology

The analysis is structured around four research questions chosen before looking at the data, each targeting a different hypothesis about delay drivers (carrier, time-of-day, geography, and operational metrics). Findings are framed in terms of effect size and predictive strength, not just descriptive averages, so the dataset can serve as a starting point for downstream regression modeling on arr_delay.

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Dataset Overview

• Source: Kaggle — Flight Data 2024
• Size: 100,000 rows sampled, 35 columns
• Target Variable: arr_delay — Arrival delay in minutes (positive = late, negative = early)
• Goal: Predict flight arrival delay in minutes based on scheduled timing, departure performance, and operational metrics.

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Data Cleaning Decisions

• Removed cancelled flights (cancelled == 0) — they have no arrival time
• Removed rows with missing arr_delay values
• Removed extreme outliers: delays below -100 or above +300 minutes (less than 0.3% of data)
• No duplicate rows were found

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Key Findings

• Flights that depart late almost always arrive late (0.90 correlation between dep_delay and arr_delay)
• Delays compound throughout the day and peak around evening hours
• Florida airports (MIA, MCO) showed the highest average delays in the sample
• Distance and airtime had almost no relationship with delays
• Regional and premium carriers outperformed budget carriers on average

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Target Variable: arr_delay

arr_delay represents the difference in minutes between scheduled and actual arrival time.

• Positive values → flight arrived late
• Negative values → flight arrived early
• Zero → flight arrived exactly on time

Since arr_delay is continuous, predicting it is a regression task.

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Research Questions & Insights

Q1: Which airlines have the highest average arrival delay?

Airlines differ significantly in scheduling discipline and operational efficiency. Comparing average arrival delay per carrier surfaces the most and least punctual operators.

Insight: B6 (JetBlue) and NK (Spirit) post the highest average delays (~6–9 minutes late). YX (Republic Airways), 9E (Endeavor — Delta-owned regional), and DL (Delta) consistently arrive ahead of schedule. Budget and low-cost carriers run materially later than regional and full-service operators.

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Q2: Does the time of day affect arrival delays?

Late-day flights may inherit delay from earlier rotations — the "delay snowball" effect.

Insight: Early morning flights (5–6am) arrive ahead of schedule, with low airport congestion. Average delay grows steadily through the day and peaks around 7pm, confirming the cascade hypothesis.

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Q3: Which departure airports have the highest average arrival delay?

Insight: Florida airports (MIA, MCO) are the most delay-prone, consistent with heavy tourist traffic and frequent summer thunderstorms. MSP, BOS, and ATL perform better on average.

🗺️ Open the interactive map

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Q4: How do numerical features correlate with arrival delay?

A correlation heatmap identifies the strongest numeric predictors of arr_delay.

Insight: dep_delay correlates 0.90 with arr_delay — by far the strongest predictor. carrier_delay (0.61) and late_aircraft_delay (0.63) contribute meaningfully. Notably, distance and air_time show near-zero correlation with delays — flight length is not a meaningful driver of lateness.

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Limitations & Next Steps

Limitations

• The 100,000-row sample is drawn from a single year (2024) and may not generalize to years with different weather or operational patterns
• Cancelled flights were excluded, so the analysis describes delay among completed flights, not overall reliability
• dep_delay is highly predictive of arr_delay but is itself a downstream variable — useful for in-flight prediction, less so for pre-departure forecasting
• Carrier-level findings reflect 2024 only and should not be read as long-term performance rankings

Next Steps

• Build a regression model excluding dep_delay to isolate truly pre-departure predictors (origin airport, scheduled hour, carrier, day of week)
• Layer in weather data to test how much of the airport-level variation is structural vs. weather-driven
• Extend to multi-year data to separate stable carrier effects from year-specific operational shocks

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Author

Avihay Amor — linkedin.com/in/avihay-amor

Files in this Dataset

• flight_data_2024_sample.csv — cleaned sample of 100,000 flights
• flight_delay_analysis.ipynb — full analysis notebook
• q1_airlines.png — bar chart: average delay by airline
• q2_time_of_day.png — bar chart: average delay by hour
• q3_map.html — interactive map: delay by departure airport
• q4_heatmap.png — correlation heatmap