Implementing TANAGRA for the Visualization and Analytical Interpretation of E-commerce Datasets

The multidisciplinary study of data mining is crucial to artificial intelligence and computer science because it allows researchers to glean useful insights from massive data sets; these datasets are essential to online shopping platforms. This field investigates the ways in which data mining. Consumer impression analysis, a rich source of qualitative data reflecting customer attitude, is the center of this study. Researchers use sophisticated natural language processing algorithms to sift through customer reviews in search of patterns and key opinion leaders. In order to provide complex insights on customer behavior, this study dives into the world of sentiment analysis and goes beyond just gathering comments. Unstructured textual data may be dynamically analyzed using ML approaches in conjunction with NLP. This method can automatically decipher intricate client data. These methods, when put together, allow for the extraction of valuable information that are critical for the development of business strategies and the improvement of digital marketplace customer interaction models. An all-inclusive data mining technique known as TANAGRA is used in the investigation. It handles problems caused by the scale and variety of big e-commerce datasets via analysis. This study's findings provide insight into the nuances of consumer behavior and point the way toward better user experience and e-commerce strategy optimization. To better understand how customers, behave on e-commerce sites, the research employs Data Mining, ML, and NLP. As a result, Computer Vision is able to analyze more complex data patterns, and AI predictive analytics get fresh understanding.