Module Code: MSIN0025 2021-2022 Module Title: Data Analytics II Assignment: Individual Assignment Section Section Marks FAIL PASS MERIT DISTINCTION DISTINCTION+ Problem Definition 10% Missing or incomplete problem definition; unclear or unstructured problem definition Context, business problem and data loosely identified Context, business problem and data clearly identified Context, business problem and data explored with clarity and understanding Highly professional and clear introduction, exploring context, business problem and data Data Description/ Data Visualization 30% Missing or incomplete data description and data visualization Data description and visualization are included, but lack specificity to the objective or business Data description and visualization are included, and align well to the business objectives Data description and visualization are clearly demonstrated, and align well to the business objectives with clear understanding Highly professional data description and data visualization, exceptionally well align to the business objectives with clear understanding Data Preparation 10% Missing or incomplete data preparation Data preparation/cleaning is included, but lack justification of the steps and reason; Lack consistency with the business objectives and the following models Data preparation/cleaning is included, associated with justification of the steps and reason Data preparation/cleaning is included, associated with clearly justification of the steps and reason, and well align to the business objectives
and the following models Highly professional data preparation/cleaning is included, associated with exceptionally well justification of the steps and reason, and exceptionally well align to the business objectives and the following models Prediction Models 40% Missing or incomplete models with configuration, not clearly defined scoring rule to measure accuracy of the models Complete individual models and the scoring rule to measure accuracy of the models and select the best model Complete individual models and ensemble model(s); justify the scoring rule to measure model accuracy and select the best model Complete individual models and ensemble model(s) with their configuration steps; clearly justify the scoring rule to measure model accuracy and select the best model Complete individual models and ensemble model(s), exceptionally well justification their configuration steps; clearly justify the scoring rule to measure model accuracy and select the best model Conclusion and Recommendation 10% Missing conclusion and recommendations Combine model results and provide conclusions of the business problems Combine model results and provide conclusions of the business problems; draw meaningful recommendations to the decision-makers Combine model results and provide conclusions of the business problems; clearly demonstrate meaningful recommendations to the decision-makers with reasons Combine model results and provide conclusions of the business problems; clearly and detailed demonstrate meaningful recommendations to the decision- makers with reasons