end-to-end machine learning project environment setup version controlgit clone the hands-on unsupervised learning git repository scientific librariesanaconda distribution of python neural networkstensorflow and keras gradient boostingversion onexgboost gradient boostingversion twolightgbm clustering algorithms interactive computing environmentjupyter notebook overview of the data data preparation data acquisition data exploration generate feature matrix and labels array feature engineering and feature selection data visualization model preparation split into training and test sets select cost function create -fold cross-validation sets machine learning models (part imodel # logistic regression evaluation metrics confusion matrix precision-recall curve receiver operating characteristic machine learning models (part iimodel # random forests model # gradient boosting machine (xgboostmodel # gradient boosting machine (lightgbmevaluation of the four models using the test set ensembles stacking final model selection production pipeline conclusion iv table of contents