The student will be evaluated based on their proficiency in key stages of the data science workflow, including data cleaning, data preparation, data preprocessing, and model training. Their ability to handle missing values, remove inconsistencies, and standardize datasets will be assessed in the data cleaning phase. In data preparation, their skills in feature selection, transformation, and data integration will be considered. The evaluation will also focus on their expertise in data preprocessing, including normalization, encoding categorical variables, and handling imbalanced data. Finally, the student's ability to train models effectively, optimize hyperparameters, and assess model performance using appropriate metrics will be a crucial aspect of the evaluation.