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Produced an in-depth analysis of commercial flight delays, passenger travel habits and cascading delays between airports using both Python and R programming languages. Implemented a logistic regression learning algorithm using scikit-learn (sklearn) and Mlr3 to predict future flight delays in real world scenarios.

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Flight-data-analysis-using-Python-and-R

Produced an in-depth analysis of commercial flight delays, passenger travel habits and cascading delays between airports using both Python and R programming languages. Implemented a logistic regression learning algorithm using scikit-learn (sklearn) and Mlr3 to predict future flight delays in real world scenarios.

Please refer the "introduction to data and list of questions" document for details about the dataset used and questions that have been tackled and analyzed.

All questions have been answered in both Python and R to showcase aptitude in both languages.

A final Report with thorough explaination of code, analysis and evaluation is available for side by side reflection along with the produced code.

Please note that the code (in both Python and R) has been extensively commented on purpose for the understanding of non-technical persons.

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Produced an in-depth analysis of commercial flight delays, passenger travel habits and cascading delays between airports using both Python and R programming languages. Implemented a logistic regression learning algorithm using scikit-learn (sklearn) and Mlr3 to predict future flight delays in real world scenarios.

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