Unless it is delayed, in which case, you can relax and read this vlog post.
How many flights are delayed each year?
How many flights are delayed at departure and how many are delayed at arrival?
Are some carriers more often delayed than others?
Are flights leaving on Thursdays more likely to be delayed than flights leaving on Sundays?
Are flights leaving Chicago airport more often delayed than flights leaving San Josè airport?
Could we use KNIME to interactively and graphically explore the airline data set and answer all - or at least most of - these questions?
Before we start with any kind of model training for more accurate predictions, it is always useful to examine the status quo and explore the kind of problem we are dealing with. This is where graphical interactive exploration comes in handy. Sunburst charts, box plots, line plots, stacked plots, scatter plots, network graphs, and other visualization techniques can offer some insights into the dataset and particularly into our delayed flights problem.
Sounds like magic? It is quite easy actually … and quite powerful!
Watch the video below for a detailed walkthrough of data visualization techniques and ways to explore your data interactively.
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