A Prediction of Customer Segmentation using Random Forest in RapidMiner

Customer segmentation is a crucial task in marketing and business strategy, enabling organizations to better understand their customer’s behavior and needs. In today’s environment, it is critical for businesses to segment their clients and take the necessary steps to compete with other businesses. Due to the abundance, each company must satisfy client demands or run the risk of losing them to other offerings. The dataset contains information about various attributes of customers, including gender, marital status, age, education, profession, work experience, spending score, family size, and other variables. The aim of this research is to predict customer segmentation using the Random Forest algorithm in RapidMiner studio based on these attributes and to identify distinct customer groups that exhibit similar characteristics. This research attempts to offer beneficial insights for customized customer experiences, targeted marketing activities, and product development. The RapidMiner tool was used to analyze the dataset. It is an open-source program for predictive analytics that supports data mining. It offers prompt responses and lets the user build models based on their needs. To forecast fresh, uncontaminated data, the model in this classification is assessed on test data after being thoroughly trained on training data. Several decision trees are constructed using various random selections of the data and features. Each decision tree acts as a professional, offering guidance on how to categorize the data. This study can be a guide to those who are willing to use RapidMiner for prediction analysis using the Random Forest algorithm.