Please go to Kaggle competitions (link: https://www.kaggle.com/competitions) and select ONE competition (project). You will use the description and data in the chosen competition to complete the below questions. You do not need to join the competition for this. An explanation file about how to use Kaggle for this coursework is attached to the Assessment folder on Moodle. Within your chosen competition (project), you should have details about the company/business, related data and what the company/competition owner would like to achieve. Take the role of an internal data translator / data analysts working in the chosen company. The selected competition is an important project in your company. Your job is to drive the project in the right direction, and hopefully, to a successfulconclusion.

REQUIREMENTS: Report explaining the use of the Cross Industry Standard Process for Data-Mining (CRISP-DM) framework in your project.

In the report, you are expected to address the following points:

1: Executive summary

2: Introduction to the company

E.g., (not limited to) company background: history, business nature, operations and future strategies (if applicable)

3: Business Understanding

Outline the problems that you are trying to solve with this project Please also use your creativity, do not just limit yourself to the problems described in the selected competition You are expected to discuss the nature of the problems identified and the potential methods/models that could be used to solve it

4: Data Understanding

E.g., What data do you have? Describe the data given Discuss the relevance/suitability of the data for the problems identified Any other perspectives that may be useful for understanding the given data? What other data do you think that may be relevant? How would you like to collect the data?

5: Data Preparation Reflecting on the data given in the selected competition, what problems might your data have? Please note that in addition to the general data problems, you are expected to discuss those problems that are closely related to the business & data given. Describe the steps you will take to cleanse and/or transform the data Other problems that might be identified in the Data preparation phase

6: Modelling Which model(s) or algorithm(s) will you use to solve the problems? Why would you select those model(s) or algorithm(s)? Discuss the strengths and limitations of your choice(s)

7: Evaluation What evaluation metrics will you use? Discuss the strengths/limitations of the metrics. Discuss: other things you may wish to consider when using the evaluation metrics proposed

8: Deployment Potential issues in the deployment of the models that will be produced in the project?

9: Conclusion Throughout your report, you are expected to illustrate the iterative nature of the CRISP-DM framework.