Analysis, prediction and forecasting of user behaviour during online shopping

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Abstract
The process by which customers search for suitable products to finish purchasing or other associated chores in a virtual shopping environment is examined in this study. All these factors come together in the payment decision, which is what a person call the integration and unity of the payment decision. This may discover probable links between a series of customer behaviors by using analysis, prediction, and forecasting analysis during online shopping. With the deployment of this technique, consumer groups could be quickly identified and segmented, allowing for the mapping of differences between them as well as a comparison of customer buying behavior across various market segments. This strategy worked quickly and effectively. The analysis, prediction and forecasting the user behavior during the online shopping. This paper includes one example of Kaggle which is based on the buying behaviors of certain products. A consumer segmentation analysis found that most customers were inactive or made infrequent purchases. There is a huge gulf between top customers and lost low-cost consumers in terms of recency, frequency, and revenue. The company should have a different approach for each section of the market. So, analysis, prediction and forecasting of User Behavior during Online Shopping must investigate with categorical approach with modified A-Priori algorithm for better explanation of the user data

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