Building Prediction Model using Market Basket Analysis

Abstract

In the recent years, analyzing shopping baskets turned out to be very appealing to retailers. Sophisticated technology made it possible for them to collect information of their customers and what they purchase. The introduction of electronic point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis (MBA). In retail business, analyzing such information is exceedingly valuable for understanding purchasing behavior. Mining purchasing patterns allows retailers to adjust promotions, store settings and serve consumers better. Predictive analysis is an advanced branch of data engineering which generally predicts some occurrence or probability based on data. Predictive analytics uses data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions. The process involves an analysis of historic data and based on that analysis to predict the future occurrences or events. A model can be created to predict using Predictive Analytics modelling techniques. The form of these predictive models varies depending on the data they are using. Predictive Analytics is composed of various statistical & analytical techniques used to develop models that will predict future occurrence, events or probabilities. Predictive analytics is able to not only deal with continuous changes, but discontinuous changes as well. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.