Predicting Magazine Sales Using Machine Learning

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. 1.0 Introduction 1.1 Problem statement In this project, we attempt to solve an important problem faced by magazine publishers today, which if successful, can dramatically help publishers maximize their profit potential. We are provided with over 10GB of data by Hearst Corporation, containing 10,929,954 store sales observations with a total of 102 features, as well as other related demographics data for predicting the sales in all of the newly-opened stores. We narrow down our scope to stores located in California because we would like to focus on applying machine learning techniques to find an excellent model rather than tackling the computational difficulties arising from using the entire dataset. After this filtering process, we obtain approximately 14,000 sales records from the past 4 years. We would like to predict the sales of individual magazine issues at 810 newlyopened stores. With a high accuracy in our resulting prediction, we can estimate precisely the number of magazine copies to be placed at these stores according to the predicted demand. This information can be used in distributing magazines to stores so that both under stock (lost sales) and over stock (cost of unsold copies) can be minimized. Sales prediction can be transformed to a multiclass classification task. In the following sections, we make use of a nonlinear multi-class support vector machine to solve our problem. 1.2 Data Description All data is stored in 5 database tables, while the main table is the SALES table where four variables in each record of SALES are used as foreign keys to locate related records in the other tables, namely ISSUES, WHOLESALERS, STORES and ZIP+4. Figure 1 Relational diagram of the dataset 2.0 Methodology 2.1 C-Support Vector Classification (C-SVC) 2.1.1 Twoclass C-Support Vector Classification Given training vectors , 1,. , , n i R i l   x in two classes, and a vector l R  y such that {1, 1} i y   , C-SVC (Boser et al., 1992; Cortes and Vapnik, 1995) solves the following primal problem: S.NO Variable S.NO Variable 1 chain_key 1 CITY 2 issue_key S.NO. Variable 2 CTRY 3 store_key 1 CAN_COV_PRC S.NO Variable Name 3 Chain Key 4 title_key 2 issue_key 1 ZIP_CODE 4 ZIP_CODE 5 wholesaler_key 3 ISS_CD 2 ZIP_4 5 ZIP_4 6 dollar_volume 4 ON_SALE_DATE 3 Households 6 STATE 7 draw 5 OFF_SALE_DATE 4 Individuals 7 CHAIN_OR_COT_DESC 8 returns 6 US_COV_PRC 5 Occupation Professional/Technical 8 Store_key 9 sales 6 Occupation Sales/Service 9 HOUSEHOLDS 7 Occupation Farm Related 10 INDIVIDUALS 8 Occupation Blue Collar 11 OCCUPATION_PROF_TECHNICAL S.NO Variable 9 Occupation Other 12 OCCUPATION_SALES_SERVICE 1 CITY 13 OCCUPATION_FARM_RELATED 2 wholesaler_key 14 OCCUPATION_BLUE_COLLAR 3 ST 15 OCCUPATION_OTHER ZIP+4 Table STORE/CHAIN Table SALES Table