IMAT2913 Data VisualizationSummative Assessment Task 1Report (60% of module coursework mark)Imagine… A supermarket is beginning to offer a...

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IMAT2913 Data Visualization Summative Assessment Task 1 Report (60% of module coursework mark) Imagine… A supermarket is beginning to offer a line of organic products. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided coupons for the organic products to all of their loyalty program participants and have now collected data that includes whether or not these customers have purchased any of the organic products. You are a data analyst and have been commissioned by the supermarket’s manager to analyse the products data and to provide the supermarket’s manager with some insights. The products data contains the attributes shown in the table below. Attribute Description CUSTID Customer loyalty identification number GENDER M = male, F = female, U=Unknown AGE Age (in years) TV_REG Television region REGION Geographic region CLASS Customer loyalty status: tin, silver, gold, platinum ORGYN Organics purchased? 1=Yes, 0=No AFFL Affluence grade on a scale from 1 to 30 LTIME Time as loyalty card member OAC Output Area Classification Demographic label sourced from ONS 1=Rural, 2=Cosmopolitan, 3=Ethnicity central, 4=Multicultural metropolitans, 5=Urbanites, 6=Surbanites, 7=Constrained city dwellers, 8=Hard pressed living. S_MT Average Weekly Spend on Meat S_FVEG Average Weekly Spend on Fruit and Vegetables S_CONV Average Weekly Spend on Convenience Food S_TOIL Average Weekly Spend on Toiletries Use the following guidelines to know what you should do with the provided dataset (refer to the Marking Grid For Summative Assessment Task 1 file for more details): Data import and understanding What you should do? Deliverable Checklist · Import data as a data frame. · Print and comment on the data type of the imported attributes. · Show and comment on the count of missing value and its percentage for each attribute. · Show and comment on the descriptive statistics of the attributes. · Explore and comment on the unique values of the attributes. · Change from one data type to another with providing justification for any change. · Print out of all python code (as figures). · Print out of all generated outputs/charts. · Your justifications and comments. Data questions & visualization AND Visualisation channels and retinal variables What you should do? Start by choosing THREE QUESTIONS you'd like visualizations to answer (they should provide insights to help take actions). Next, design a visualization chart to answer each question. Then, provide a brief description of your design (e.g., the use of data wrangling to prepare the data for visualization to find the answer for the posed question, choice of visualization plot, size, colour, scale, and other visual elements). Deliverable Checklist · Print out of all python code (as figures). · Print out of all generated outputs/charts. · A brief description and justification of your design. Do not interpret the generated visualizations. Reflection What you should do? Deliverable Checklist Show how your data visualization knowledge improved over the course and how this might be applicable in a different context. In addition, show how you can validate your design to assess its effectiveness to communicate the derived insights. · Your reflection. · Design validation. Note: Your contribution to the report should be no longer than 1,500 words. Use a minimum font size 12. 21