Leveraging Machine Learning for Customer Intelligence: An Experimental Analysis Learning Classifiers

Abstract

Listen

Translate article

The main objective of any business entity is to optimize its revenue and sustain a robust competitive stance inside the market. A detailed understanding of the company's customers is essential for achieving high revenue and market supremacy. Therefore, a key component of achieving business prosperity is effective customer analysis. To conduct efficient consumer analysis, we investigate several machine learning algorithms in this research. In order to take well-informed business decisions that will increase revenue and improve customer happiness, machine learning classifiers are essential for acquiring deep insights into consumer data. We aim to investigate at how consumer forecasts change based on the machine learning classifiers utilized by applying various classifiers. This method enables us to choose the best machine learning classifier for the consumer dataset under analysis. For consumer analysis, our experimental technique applies a variety of machine learning classifiers, such as K-NN, C4.5, Random Forest, Random Tree, Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Naive Bayes (NB). The factual findings show that, when compared to other ML classifiers, the C4.5 model obtains a higher prediction accuracy. Additionally, owing to its lower operating time, the NB model stands out as an effective option when considering time efficiency. In summary, this study investigates the use of several ML Model for consumer analysis, with an emphasis on increasing both business revenue and consumer satisfaction. The NB model outperforms the C4.5 model in terms of time efficiency, and the C4.5 model is a great performer in terms of prediction accuracy.