:: Instructor-led Classes

Data Analytics: Predicting Profitability and Customer Preferences

Course Details

In this course, you will continue to work with Danielle Sherman, the Chief Technology Officer at Blackwell Electronics. Blackwell Electronics is a successful consumer electronics retailer with both bricks & mortar stores in the southeastern United States and an eCommerce site. They have recently begun to leverage the data collected from online and in-store transactions to gain insight into their customers' purchasing behavior. Your job is to extend their application of data mining methods to develop predictive models. In this course, you will use machine learning methods to predict which potential new products that the sales team is considering adding to Blackwell's current product mix will be the most profitable. Next, you will create a model to predict which brand of computer products Blackwell customers prefer based on customer demographics collected from a marketing survey. Finally, you will present to management, explaining your insights and suggestions for data mining process improvements.

Return to the Learn By Doing page.

Are the skills in this course applicable to data mining tasks in general?

Yes! The methods that you will use in this course have wide applicability to the data mining tasks you will encounter in nearly all business sectors and other real-world applications. The skills practiced in this course represent current professional practice and include:
  • Applying data mining in ecommerce (e.g. profitability prediction, customer segmentation, product selection strategy)
  • Performing a similarity analysis
  • Preprocessing data for data mining (e.g., applying filters, addressing missing data)
  • Using data mining tools and different classifiers (e.g., k-nearest neighbor, decision trees, support vector machines) to develop predictive models
  • Applying machine learning techniques to classification and regression problems
  • Optimizing classifiers by adjusting and testing classifier parameters
  • Applying cross-validation methods
  • Assessing the predictive performance of classifiers by examining key error metrics
  • Comparing and selecting different predictive models
  • Applying predictive models to test sets
  • Presenting data mining results to management


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