How to use Data Processing for Predicting Flight Delays

Imagine a Data Analytics tool that can predict flight delays! Data Analytics (DA) technology has progressed remarkably over the last decade. According to Peter Sondergaard, if data is the oil of the 21st century then data analytics is the combustion engine that uses that oil to produce the desired output. Today, big data mining, Internet of Things, machine learning technology, and real-time computing, are commonplace terminologies used in the world of data analysis.

Data Analytics tools designed for data processing for predicting flight delays do just that. The tools access streaming data on departure airports, destinations, flight scheduled timing, weather forecasts of both the airports, political conditions, etc. in a real-time scenario and predict whether a particular flight will be delayed.


One of the biggest challenges of leveraging large amounts of streaming data is the ability of a data model to temporarily store it in a measurable format and analyze it to produce specific outputs. Once this hurdle has been successfully overcome, then it is time for testing your data analysis model for predicting flight delays.

Of course, your data model has to follow the usual “true or false” pattern. The model will generate results depending on whether the answer to a query matches the given input parameters or not. Your data model must answer the following questions so that it can predict flight delays accurately:

  • What are the results expected? – Flight delays
  • Will the flight be delayed or not? – False or True
  • Which is the departure airport?
  • Which is the destination airport?
  • What is the scheduled departure time?
  • What is the scheduled arrival time?
  • Which day of the week is the flight?
  • At what time of the day is the departure of the flight?
  • Is it a non-stop flight or is there a layover?
  • What is the name of the Carrier?

Taking these parameters into consideration, your data model will have to minimize the number of true or false results, so that it can improve the probability of accurate predictions for delayed flights. The model will look something like this:

Query 1: If the departure time (scheduled) is at 09:00 hours

Query2: If the departure airport (for example) is in the set [ATL, LAX, DFW]

Query 3: If the day of the week (for example)is [Sunday, Monday, Tuesday]

If True then: Delayed=1

If False then: Delayed=0

So if the answer is False, then the next step is:

Query 1: If the departure airport is in the set [SFO, EWR, ORD] and so on.

In a real-time scenario, data transformation becomes necessary in order to predict outcomes from streaming data. Your data model has to first analyze whether the information is relevant to the analysis or not. It must have a representative subset that contains all the necessary variables for the prediction which also takes into consideration that there will be countless probabilities. This will make your predictive analysis model a more complex one, but a more reliable and accurate one.

Once the model shows you the most probable routes (connections between two airports) that have incidences of delayed flights, you can add another parameter to give your data model an increasing edge for accurate predictions.

Query 1: How is the weather at the originating airport?
Query 2: What are the weather conditions at the destination airport?

Therefore, in machine learning the algorithm that forms the test model should contain all those parameters which would usually affect flight timings and delayed flights.


Testing the “test model” using dummy stored data is also a very important stage in data processing for predicting flight delays. This will help you to identify which parameter has the maximum influence on the output and which one gives you the most accurate predictions. This a crucial step you need to take before you try out the model on real-time streamed data.

Using the full data as a model to evaluate the same full data doesn’t make sense. So let’s use the most popular method of cross-validation (k fold) in which the stored data is broken into k number of sets by random selection. Each sample set is used as a test data for the remaining sets. The sets are then rotated to form a new sample. The previous sample joins the rest of the data being tested. Models based on these random sample subsets will hone the model to give more accurate predictions because the sets are in fact tested over and over again in a loop.

The predictions will be as follows:

  • The true-yes predictions indicate how many times the subset accurately predicted flight delays.
  • The false-yes predictions indicate how often the subset predicted incorrect information on delayed flights.
  • True-no indications show how many times the model accurately predicted that there was no delay in the flight timings.
  • False-no results will indicate how many times your model erroneously predicted a no-delay status of flights.

The sensitivity of your data model will determine its efficacy. The more sensitive it is to any changes in data, the more it can be fine-tuned. The more it is tweaked through loop-testing the more are the chances that the model will produce accurate results. In other words, your model will reach its peak efficiency when it produces the highest number of “true-yes situations” and “true-no situations” correctly. Once your data model has reached this stage it is ready to be saved and used on real-time streamed data.

To sum up, data processing for predicting flight delays is a complex process. But very accurate predictive models can be built so that real-time streamed data can be collected and analyzed. Detail information about scheduled flight timings, departure airports, destination airports, carriers, weather conditions at both the airports and other factors that affect flight departures and arrivals will be the key parameters of your Data Analytics model. Despite their apparent complexity, these predictive data models can be used to accurately predict flight delays.

Leave a Comment