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Predicting Magazine Sales Using Machine Learning

By Mei Ling (zan Chu, Freeman Fan and Yisha Peng


In this project, we apply machine learning techniques to a real-world problem of predicting magazine sales, i.e. the number of magazine copies to be placed at newly-opened newsstand locations using past data gathered from existing stores. Given the raw data from Hearst Corporation regarding store sales, store locations, demographics and other related facts, we designed a nonlinear multi-class SVM classifier to predict the amount of magazine sales at newly-opened stores. A C-SVC model with radial basis function (RBF) kernel is built for analyzing this highly heterogeneous set of data. Cross-validation via grid-search is applied for parameter tuning. In view of the nature of our problem, Root Mean Square Error (RMSE) is used to measure the prediction accuracy. The theoretical framework and experimental results for this problem are discussed in our article. 1.1 Problem statement In this project, we attempt to solve an important problem faced by magazine publishers today, which if successful, can dramatically hel

Year: 2013
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