Performance Study of Proposed Predictive Data Mining Model for analysing Online Customer Buying Behaviour

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Teena Vats, Dr. Kavita Mittal


These days web-based business climate assumes a significant part to trade product information between shoppers regularly with others. Precisely foreseeing client buy designs in the web-based business marketplace is the basic place to use the information mining. To accomplish high benefit in internet business, the connection among client and product are vital. Additionally, numerous web-based business sites increment quickly and immediately and contest has become quite recently a just a click away. For that reason, the significance of remaining in the business, and further developing the benefit needs to precisely anticipate buy conduct and focus on their clients with customized administrations as per their inclinations. In this paper, an information mining model has been planned to improve the exactness of foreseeing and to observe affiliation rules for incessant thing sets. Various algorithms have been used to calculate the customer behaviour like Tree Classifier Boost Classifier, gradient Boosting Classifier, Random Forest Classifier. Planned model has been verified on three city Yangon, Mandalay, Naypyitaw Mall’s sales dataset and the results shows that data uses is 94.0%.

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