While it might be unorthodox to post code from a job interview coding challenge, I not only found this challenge enjoyable, but also a good opportunity to showcase various modeling techniques.

• data: this folder contains the CSV files used in the coding challenged. I believe this was a combination of real and synthesized/anonymized data

• Tasks-BMW.pdf: actual writeup describing each section of the coding challenge. There were four tasks, so I created separate notebooks for tasks 1, 2, and combined 3-4.

• Task1.ipynb: Ensemble of binary classifiers for a tabular dataset of mixed data types. Classifiers included SVM, naive Bayes, and a gradient boosted classifier. All models were hyperoptimized using sklearn's grid search method. The ensembler was based on hard voting of the predictors.

• Task2.ipynb: Dimensionality reduction using latent semantic analysis, unsupervised outlier detection using isolation forests, and nearest neighbor search using k-d trees

• Task3-4.ipynb: Creates some functions to calculate elapsed time between timestamps