Prediction of Customer Spending Score for the Shopping Mall using Gaussian Mixture Model comparing with Linear Spline Regression Algorithm to reduce Root Mean Square Error

Abstract

The aim of the study is to predict the customer spending score using machine learning algorithms and the objective of this research is to improve the performance of Root Mean Square Error (RMSE). Materials and Methods: Two algorithms are used in this research work with 51 samples using 80% of g power value and the shopping mall customer spending data were collected from various web sources with recent study findings. To predict the customer spending score performance of the Linear Spline Regression (LSR) algorithm has found 32.12% of performance and the outcome of this research work significantly useful to improve the customer spending score prediction with the Gaussian Mixture Model (GMM) algorithm. Results: This research study found 15.26% of RMSE for Mall innovative customer spending score prediction using a GMM algorithm with a significant value of two tailed tests is 0.001 (p<0.05) with 95% confidence interval. Conclusion: This research work predicts the best result using GMM algorithm on customer spending score improved with various parameters than the LSR algorithm.