ABSTRACT
This research study attempts to conduct a complete Coca-Cola sales analysis using data mining techniques to extract important insights from historical sales data and consumer information. The major purpose in the competitive consumer products business is to discover opportunities for improving sales efficiency and enabling informed decision-making for strategic initiatives. For a comprehensive analysis, the study employs a wide dataset comprising product sales, geographic information, customer demographics, and relevant variables, as well as external elements such as economic indicators and consumer trends. Rigorous pre-processing techniques, such as data cleaning, integration, transformation, compression and pattern generation are utilized to assure data quality and consistency. These methods deal with errors, deal with missing numbers, and normalize the data for robust analysis. Sales data analysis employs various data mining techniques, including classification, association rule mining (ARM), similarity analysis, and predictive models like decision trees and regression, to identify pertinent patterns and insights. The results of this investigation offer Coca-Cola important insights, such as cross-selling opportunities, high-potential market segment identification, and precise sales forecasts via predictive modeling. The results of this study have noteworthy consequences for customized marketing approaches, interdisciplinary cooperation, and the requirement for ongoing evaluation and modification to guarantee long-term prosperity in the sector.
