Final group project done as a part of machine learning class with the goal of classifying and predicting whether the bought vehicle will be a good or a bad buy (have some defect in the near future).

• Performed missing value imputation using various techniques
• Performed feature selection through statistical tests and embedded methods
• Created and used scikit-learn pipelines
• Transformed and prepared data to be suitable for model building
• Used different undersampling and oversampling techniques to balance the dataset
• Evaluated and compared different machine learning models using cross-validation
• Hyperparameter tuning for optimization
• Used several groups of ensemble algorithms (stacking, bagging and boosting)