Flight Delay Prediction using Machine Learning Techniques | Python Project | Machine Learning | Artificial Intelligence | Image Processing | IEEE

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Abstract:

Flight data is inherently complicated, and in virtue of his complexity departure and arrival delay are unpredictable. Every year a number of flights get delayed or cancelled due to several reasons. These reasons include weather conditions, security, carrier delays and so on. Re-boarding of aircraft due to security breach, defective screening equipments or long lines at screening areas can account for delay in flights. Extreme calamities like blizzards, hurricanes and tornados will inevitably lead to flight delays and even cancellations. Flight delays can prove to be somewhat costly to National Airspace System (NAS) according to the study by Klein. In 2014, reports suggested that NAS accounted for 23.5% of total delay minutes. In order to reduce these costs, researches have been conducted and a profound analysis has been performed or the prediction of air traffic delays. Based on the analysis, more productive and competent air traffic management approaches could be planned and implemented. Air carrier delays related to circumstances like maintenance or crew problems, aircraft cleaning and baggage loading can also contribute to flight delays. Proper choice of air carriers can help avoid schedule delays and many studies have been performed towards that objective. Air carrier delays in 2014 accounted for 30.2% of total delay minutes. Since most of the delays are caused due to sudden and unanticipated circumstances, data analytics and statistical machine learning has a motivated researcher to address this problem. But historical data of flight patterns can help build predictive models that can give mostly accurate prediction of a certain flight arriving or leaving a certain airport.
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Technology:

• python
• pdk
• Machine Learning
• Artificial Intelligence
• MySql

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