The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.

This is a custom library for data processing, visualization and machine learning tools.

Split train/val/test coco dataset and Adjust annotations & categories.

Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet

The aim is to decrease maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionizing model using pipelines