Oct 2021 ~ FedEx Express

Length:   1,5mo (at 0.5 FTE)

Programming language:   Python (Pandas, NumPy, Matplotlib, seaborn, scikit-learn, os, time)

Data:   sklearn datasets generated for testing purposes

Problem description:
Program a class in OOP fashion that contains several methods for evaluating the performance of Machine Learning models

Approach & Results:
As there are multiple Machine Learning models used at FedEx, the developers could benefit from a class consisting of functions for ML model performance evaluation. With such a module available, the evaluation procedure could be simplified and standardized, while the Data Science team could save time and make well-informed decisions about model training. Accordingly, a class was built to include several evaluation functions ranging from standard metrics (e.g. Accuracy, Recall, F1, FNR), to curve plotting (e.g. Learning curve, Validation curve, Precision-Recall curve, ROC) and feature importance (e.g. tree models, permutation importance), by leveraging the scikit-learn library. Also, an extensive documentation was written to ease the functions utilization, followed by error handling to make the module forestall anticipated errors if possible or provide the user insights into what went wrong.

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