please answer all questions with the same requirements mentioned.

Module Learning Outcomes The following LOs are achieved by the student by completing the assignment successfully 1) Apply descriptive and predictive mining techniques 2) Use appropriate data mining techniques and tools for knowledge discovery Assignment Objective The objective of this assignment is to enable the student to gain an understanding of the concepts of data mining, various phases involved in data mining and apply mining techniques for knowledge discovery. Assignment Task Task 1. Perform a data mining task by following the CRISP-DM methodology. The entire task needs to be executed in a programming language of your choice. Submit a report that explains the solution of the tasks shown below from (i) to (vi). Include the screenshots of codes and/or graphs in the report to illustrate the application. (100 marks) :AMO_TEM_035_04 Page 10 Mining and Predictive Analytics (COMP 30044) - Fall - 2022 - CW2 (Assignment) - ALL - QP (i) Identify a problem that involves a Data Mining task. (10 marks) (ii) Collect the dataset and analyze the data by applying one descriptive exploration that shows the relationship between the attributes in the dataset from which you can draw meaningful information out of your dataset. Relevant screen shots of codes and/or graphs are required. (10+20=30 marks) (iii) Analyze the data and apply one data wrangling method to clean the data. Relevant screen shots of codes and/or graphs are required. (20 marks) (iv) Analyze the task and apply any one model for predictive mining as appropriate to your data mining problem. Relevant screen shots of codes and/or graphs are required. (15 marks) (v) Evaluate the model based on any one metric. Relevant screen shots of codes and/or graphs are required. (20 marks) (vi) Suggest a deployment method for the model. (5 marks) Note to students: The problem identified can be a business problem from any area of research (example: healthcare/finance/marketing/transportation etc) of your choice. The approval of the problem with the dataset should be sought from the module instructor by week 8 . The dataset aligned to the chosen problem if available freely (example: https://www.kaggle.com/datasets), can be collected or the student can prepare a dataset of their own by aligning it to the problem identified. Rules \& Regulations: - All resources should be cited using APA 7th edition referencing style. - The final assignment must have a Title page, Table of Contents, References/ bibliography using APA 7th edition referencing style and page numbers. - Title Page must have Assignment Name, Module name, Session, your name, ID, and the name of the faculty. - Softcopy in word format (for Task 1) is to be submitted through Turnitin link on MEC Learn (Moodle). Guidelines: - Assignment must be computer typed. Font - Times New Roman Font - Style - Regular Font - Size - 12 Heading should be with Font Size 14, Bold, Capital and Underline. - Explain with suitable diagrams wherever required. Diagrams must be drawn using suitable software or by pencil. - Each student has to do the assignment individually - You can refer books in E-Library or use internet resource. But you should not cut and paste material from internet nor provide photocopied material from books. The assignment answers should be in your own words after understanding the matter from the above resources. Assessment Evaluation Criteria