Modelling Search Habits on E-commerce Websites using Supervised Learning

Consumers are going through a huge transition in terms of their choices as well as the propensity to spend. People increasingly travel outside the country and understand the spectrum of products or services available in other countries. This has given a huge impetus to E-commerce companies and start-ups offering a variety of products and services. The continuous development of E-commerce platforms and the convenience of purchasing goods and services has increased the customer base continuously. The broad objective of the study is to extract information from consumer searches and use it analytically for driving the business in the future. The purpose of the research is to use supervised classification techniques to categorize product related search queries into category (level 1) and subcategory (level 2), which is further required to derive shopping patterns and trends among the consumers. In this paper, we explore the various multiclass classification techniques, like Naïve Bayes, Random Forests, and SVM. The Naïve Bayes classification at the category (level 1) and subcategory (level 2) outperformed the other algorithms to achieve maximum accuracy of the search query classification.