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A supermarket is beginning to offer a line of organic products. The supermarket’s management would like to determine: data mining Assignment, UOS, UK
| University | University of Southampton (UOS) |
| Subject | data mining Assignment |
SCENARIO: THE ORGANICS DATASET
⦁ A supermarket is beginning to offer a line of organic products. The supermarket’s management would like to determine which customers are likely to purchase these 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 miner and have been commissioned by the supermarket’s manager to analyse the ORGANICS data and to provide the manager with the best model that s/he should use to identify the customers who are likely to buy the supermarket’s new line of organic products.
The analysis you are conducting will represent the first flow of the virtuous cycle of data mining.
You will be assessed on producing a technical, well-structured, comprehensive but concise report to the manager of the supermarket. This report is broken up into five activities, four of which you are encouraged to do biweekly and self-assess your work using the lab journal. The final activity integrates the pieces into one report detailing:
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Activity 1: Week 3 – Week 4
⦁ Develop a description of the business problem and appropriate data mining problem and describe a data mining framework that is appropriate for your brief. Identify the target variable.
⦁ Make appropriate use of Exploratory Data Analysis on your data set to develop insights that will inform your data mining process suggest any transformations which might be appropriate.
Activity 2: Week 5 – Week 6
⦁ Apply regression analyses to your dataset including the full model and the Selection Methods: Forward, Backward and Stepwise. Develop a regression equation which includes only significant parameters at the 95% confidence interval.
⦁ Conduct a Decision Tree analysis on the data set, vary the default parameters and present an interpretation of your results. Identify the target path(s) and critical path.
Activity 3: Week 7 – Week 8
⦁ Conduct a Neural Network analysis on the data set, vary the default parameters and present an interpretation of your results.
⦁ Choose to try different neural network architectures. Identify the most important weights together with a diagram identifying the neural network architecture.
Activity 4: Remaining time
⦁ Justification of your final selected model, by considering appropriate data mining strategies: Cumulative Lift Charts, Non-Cumulative Lift Charts and Diagnostic Charts.
⦁ Conclusions
⦁ Recommendations on how to improve the quality of the supermarket’s data collection process in the future, to enable you as a data miner the opportunity to improve on the accuracy of the data mining model in further flows of the data mining cycle. Develop and integrate your activities into a full technical report.
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