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Employed cutting-edge Machine Learning and Deep Learning algorithms, including SVM, Random Forest, and Neural Networks, to develop a dynamic framework for assessing pilots' qualification using Python and prominent ML libraries such as scikit-learn, TensorFlow, and PyTorch.

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FAA-Airman-Qualification-Prediction

Author: Nasri Binsaleh & Uma Maheshwar Reddy Jangalapalli

Considering that the Federal Aviation Administration's (FAA) existing license renewal guidelines were established in the early 1900s and no longer reflect current realities. There is a need for a more contemporary approach to qualifying pilots.

This project employed cutting-edge Machine Learning and Deep Learning algorithms, including SVM, Random Forest, and Neural Networks, to develop a dynamic framework for assessing pilots' qualification using Python and prominent ML libraries such as scikit-learn, TensorFlow, and PyTorch.

See Final_Report.pdf for full details.

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Employed cutting-edge Machine Learning and Deep Learning algorithms, including SVM, Random Forest, and Neural Networks, to develop a dynamic framework for assessing pilots' qualification using Python and prominent ML libraries such as scikit-learn, TensorFlow, and PyTorch.

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