V. CONCLUSION In this paper, we research SVM methodology from widespread sources to develop a deeper understanding of this machine learning technique and apply this knowledge upon a chosen dataset. In our own application, we uncover Amazon consumer purchasing habits by classifying the relationship between products and their co-purchases, which are predicted from Support Vector Machines (SVM) and k-means. By deciphering from product titles, descriptions, categories and unique identifier (ASIN), we wield Amazon metadata and analyzing purchasing Amazon’s patterns from alphanumeric inputs. Our parsimonious text classification model allows us to predict with high accuracy on co-product relations. For future research endeavors, we would be able to flexibly expand our analysis by including an additional row in the initial CSV data entry process to include other attributes with associated ASINs, such as or instead of This would lend to changing the categories descriptions titles. function within the R code to make the last row. The aftermath product.container init testSize would construct a prediction on whether, for instance, a certain product would show up pairwise from with other products. In addition, this analysis could become more granular and also_bought thorough if we further investigated the True co-product relationships. also_bought_count BIBLIOGRAPHY
V. CONCLUSION In this paper, we research SVM methodology from widespread sources to develop a deeper understanding of this machine learning technique and apply this knowledge upon a chosen dataset. In our own application, we uncover Amazon consumer purchasing habits by classifying the relationship between products and their co-purchases, which are predicted from Support Vector Machines (SVM) and k-means. By deciphering from product titles, descriptions, categories and Amazon’s unique identifier (ASIN), we wield Amazon metadata and analyzing purchasing patterns from alphanumeric inputs. Our parsimonious text classification model allows us to predict with high accuracy on co-product relations. For future research endeavors, we would be able to flexibly expand our analysis by including an additional row in the initial CSV data entry process to include other attributes with associated ASINs, such as categories or descriptions instead of titles. This would lend to changing the product.container init function within the R code to make testSize the last row. The aftermath would construct a prediction on whether, for instance, a certain product would show up pairwise from also_bought with other products. In addition, this analysis could become more granular and thorough if we further investigated the True also_bought_count co-product relationships. BIBLIOGRAPHY