| --- |
| license: mit |
| task_categories: |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - aviation |
| - flights |
| - delays |
| - machine-learning |
| size_categories: |
| - n<1K |
| --- |
| |
|  |
|
|
| # 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`. |
|
|
| --- |
|
|
| ## Dataset Overview |
|
|
| - **Source:** Kaggle — [Flight Data 2024](https://www.kaggle.com/datasets/hrishitpatil/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. |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## 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. |
|
|
| [](/datasets/avihayamor/US_flight-delay-2024/blob/main/q1_airlines.png) |
|
|
| **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. |
|
|
| --- |
|
|
| ### Q2: Does the time of day affect arrival delays? |
|
|
| Late-day flights may inherit delay from earlier rotations — the "delay snowball" effect. |
|
|
| [](/datasets/avihayamor/US_flight-delay-2024/blob/main/q2_time_of_day.png) |
|
|
| **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. |
|
|
| --- |
|
|
| ### Q3: Which departure airports have the highest average arrival delay? |
|
|
| [](/datasets/avihayamor/US_flight-delay-2024/blob/main/q3_map.png) |
|
|
| **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](https://huggingface.co/spaces/avihayamor/flight-delay-map-2024) |
|
|
| --- |
|
|
| ### Q4: How do numerical features correlate with arrival delay? |
|
|
| A correlation heatmap identifies the strongest numeric predictors of `arr_delay`. |
|
|
| [](/datasets/avihayamor/US_flight-delay-2024/blob/main/q4_heatmap.png) |
|
|
| **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. |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## Author |
|
|
| **Avihay Amor** — [linkedin.com/in/avihay-amor](https://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](https://colab.research.google.com/drive/1iqPdkwt78yzJ301YtzQ56ZjyC_DJiX6C?usp=sharing) |
| - `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 |
