Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
- Title
- Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
- Creator
- Prathiba L.; Raja S.; Umadevi A.; Sucharitha M.M.; Basha M.S.A.
- Description
- This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE.
- Source
- 2024 3rd International Conference for Advancement in Technology, ICONAT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Academic Achievement; Machine Learning Models; Smart Phone; Student Health
- Coverage
- Prathiba L., Master of Business Administration Ashoka Women's Engineering College, Andhra Pradesh, Kurnool, India; Raja S., R&D Institute of Science and Technology Avadi, Vel Tech Rangarajan Dr. Sagunthala, Chennai, India; Umadevi A., Alpha College of Engineering, Poonamallee, Department of Management Studies, Chennai, India; Sucharitha M.M., Md Shaik Amzad Basha Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM (Deemed to Be University), GITAM School of Business, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035417-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Prathiba L.; Raja S.; Umadevi A.; Sucharitha M.M.; Basha M.S.A., “Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/18999.