Data Analytics
Applying data mining in e-commerce (e.g. profitability prediction, customer segmentation, product selection strategy) 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 Using the R statistical programming language Using the caret package for R 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.