The Developer's Guide to Normalizing Historical Airline Flight Data for Machine Learning

Source: Dev.to

In the world of data science, there is an old axiom that holds truer in aviation than perhaps any other industry: Garbage In, Garbage Out.
If you are building a predictive model—whether to forecast flight delays, optimize supply‑chain logistics, or power a dynamic pricing engine—the architecture of your machine‑learning (ML) model is secondary to the quality of your training data. Aviation data is notoriously “dirty”: changing call signs, shifting time zones, complex codeshare agreements, and operational irregularities can all introduce noise. Feeding raw, unprocessed JSON directly into a neural network or regression model will lead to unreliable predictions.

This guide explores the specific data‑engineering pipeline required to normalize historical airline flight data so that your models reflect the reality of the skies.

The First Hurdle: The “Codeshare Trap”

The most common mistake developers make when ingesting aviation data is treating every flight number as a unique physical event. A single aircraft flying from New York (JFK) to London (LHR) might carry several marketing flight numbers simultaneously (e.g., AA100, BA1500, IB4000). This is known as a codeshare.

Why this breaks Machine Learning

• Each marketing number represents the same physical flight, so duplicate rows inflate the dataset and bias the model.

The Normalization Fix

1. Ingest the raw data.
2. Filter for is_codeshare: false.
3. Master the dataset to the operating flight number (the carrier actually operating the aircraft).

By isolating the “metal” rather than the tickets sold, your training data reflects physical reality.

Feature Enrichment with an Airport Data API

Historical flight logs typically contain events such as takeoff, landing, and delay minutes. To predict why a delay happened, you need contextual information about the airport environment.

Out‑of‑the‑Box Idea: Runway & Terminal Weighting

• number_of_runways
• airport_elevation
• terminal_complexity_score

Logic: A single closed runway at a two‑runway airport cuts capacity by 50 %—a catastrophic event—whereas the same closure at a five‑runway hub is a minor inconvenience. Enriching your dataset with these infrastructure features enables the ML model to weigh airport resilience, leading to far more accurate delay predictions.

Time Normalization: The UTC vs. Local Paradox

Aviation operates on Coordinated Universal Time (UTC/Zulu), but passenger behavior, airport staffing, and rush‑hour traffic follow local time.

Strategy

• Sequential Time (UTC): Use for calculating flight duration, turnaround times, and linking chronological events.
• Cyclical Time (Local): Convert arrival/departure times to the local IANA timezone (e.g., America/New_York). Extract human‑centric features such as hour_of_day (0‑23) and day_of_week.

This lets the model learn patterns like “Flights departing JFK on Fridays between 16:00 – 19:00 local time have a high probability of taxi‑out delays.”

Handling Edge Cases: Diversions and “Ghost Flights”

In standard datasets, a diverted flight can appear as a data error (e.g., scheduled for ORD but recorded as arriving at IND).

Fix

Create a route_integrity boolean flag during preprocessing:

Usage:

• Exclude diversions when training a standard schedule‑reliability model.
• Segregate them into a specialized “Anomaly Detection” dataset if you want to model unusual events. Training a general model on diverted flights introduces noise that degrades accuracy.

The “Turnaround” Feature: Chaining the Tail Number

Most basic flight trackers treat flights in isolation, but a flight is often one link in a chain. By stitching data together using the Aircraft Registration (Tail Number), you capture knock‑on effects.

The “Chain Reaction” Feature

• Incoming delay buffer:
  • Scenario: Flight B is scheduled to depart at 14:00.
  • Data: The aircraft for Flight B is currently on Flight A, which is delayed and lands at 13:50.
  • Calculation: Only 10 minutes remain for deplaning, cleaning, and boarding—insufficient time, leading to a delay for Flight B.

Tracking the specific aircraft rather than just the route lets the model learn these cascading delays.

From Raw Logs to Predictive Intelligence

The difference between a dashboard that looks nice and one that drives business decisions lies in data engineering. Raw aviation data records what happened; normalized data—stripped of codeshares, enriched with airport context, and chained by aircraft histories—maps what will happen. By following these normalization steps, you ensure that your ML models train on clear signals, not noise.

Frequently Asked Questions

Q1: How far back should my historical data go for training?
A: It depends on the model’s purpose, but a minimum of 12 months captures seasonal patterns; 3–5 years provide robustness against outliers.

Q2: How do I handle missing data points in historical logs?
A: Common strategies include imputation with median/mean values, forward‑filling based on previous records for the same tail number, or flagging missing entries and letting the model learn from the absence.

Q3: Can I use this data for sustainability reporting?
A: Yes—once normalized, the dataset can be joined with fuel‑burn and emission factors to produce accurate carbon‑footprint metrics.

Recommended Resources

• Aviationstack – Real‑time and historical flight data in JSON.
• FlightAware (AeroAPI) – Comprehensive flight tracking with extensive coverage.
• OAG – Global airline schedules and performance data.

These providers offer robust APIs that deliver the raw logs needed to build the normalization pipeline described above.