A Machine Learning Analysis Of Advertising In Magazine
Journal Title: International Journal of Business and Management Invention - Year 2018, Vol 7, Issue 6
Abstract
In this paper, I investigate the factors that cause magazine advertising costs to vary using machine learning models. In particular, I build ordinary least squares (OLS) regression models to predict magazine advertising costs given features such as magazine circulation, income of readers, and gender. I evaluate the models using the paired t-test and adjusted R2 . The adjusted R2 is a more informative measure than the commonly used R2 . I find circulation and median income of readers to be significant. The adjusted R2 is found to be 0.6734, suggesting that 67.34% of the variation in advertising costs can be explained by the independent variables.
Authors and Affiliations
Andy W. Chen
Keywords
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- EP ID EP400662
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