DATA VISUALIZATION USING THE RAPID MINER APPLICATION TO EVALUATE SALES PATTERNS

Abstract

In today's digital era, data has become a valuable asset for many companies, including those in the retail and sales sectors. Using the right method to interpret and understand this data is the key to improving business strategy and making the right decisions. In this context, data visualization plays a crucial role as a tool for presenting complex information in an intuitive and easy-to-understand way. This study aims to utilize the RapidMiner application in visualizing data for evaluating sales patterns. Using a sales dataset from a retail company, we perform a series of analytical processes using RapidMiner. Various visualization techniques, such as bar graphs, line charts, heat maps and more, applied to extract insights from sales data and identify certain patterns. The visualization results show several interesting findings, such as the season or period with the highest sales, the best-selling products, and the relationship between sales variables. Through this visualization, we are able to provide strategic recommendations to companies to increase revenue and optimize marketing strategies. The conclusion of this study is that RapidMiner, with its capabilities in data visualization, is an effective tool to help companies understand sales patterns and make data-driven decisions. as well as the relationship between sales variables. Through this visualization, we are able to provide strategic recommendations to companies to increase revenue and optimize marketing strategies. The conclusion of this research is that RapidMiner, with its data visualization capabilities, is an effective tool to help companies understand sales patterns and make data-based decisions. as well as the relationship between sales variables. Through this visualization, we are able to provide strategic recommendations to companies to increase revenue and optimize marketing strategies. The conclusion of this study is that RapidMiner, with its capabilities in data visualization