Optimizing eCommerce Data

Abstract

The process of gathering and analyzing data, on variables in a manner to address research inquiries, test hypotheses and evaluate results is referred to as data collection. After obtaining the required data the next important step is pre-processing it. Data pre-processing involves converting data into a dataset before running algorithms. It prepares the dataset by handling missing values, noisy data and inconsistencies. Sometimes additional adjustments such as handling outliers and creating subcategories are needed for generating outcomes. This process can be seen as a step in organizing data effectively. The methods mentioned above demonstrate that research entails steps typically carried out manually or using applications by researchers. This article will explore an instances where Artificial Intelligence (AI) and Machine Learning (ML) are utilized for cleaning and pre-processing data. By examining the data AI and ML can detect patterns efficiently. Visual representations play a role, in understanding customer satisfaction and happiness levels within a shopping platform