In this data analysis project, we'll dive into the "Airline Reporting Carrier On-Time Performance Dataset" from IBM : https://developer.ibm.com/exchanges/data/all/airline/
Flight cancellations and delays can cause big problems for passengers and airlines. It's really important to try and prevent these issues because they make customers unhappy and airlines lose money. In 2022, all the airlines together lost $6.9 billion. Avoiding these disruptions helps airlines run better, keeps their good reputation, and makes travelers feel more confident about flying.
In this data analysis project, we'll dive into the "Airline Reporting Carrier On-Time Performance Dataset" from IBM, which contains information about domestic flights in the United States from 1987 to 2020. Our main goals for this project are to learn about how often airlines are on time, figure out why flights get delayed, and create a smart tool that can predict delays. By doing this, we want to find useful information and ideas to make air travel in the US more reliable and efficient.
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Generate an insight of the dataset, try to understand the main cause of flight delayed in order to anticipate them.
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Generate a model of machine learning in order to calculate the delay a flight will encounter (in generation)