Trending Sales in E-commerce Using Machine Learning: A Study Referring to Mobile Phone Set (The Lumbini Journal of Business and Economics, 2025)
Purpose:This is an investigation to fund the influence of price, technical features,and marketing strategies, on the sales volume of mobile phone sets and developed a model for mobile phone companies using machine learning techniques. Method: Starting from simple linear regression, this research work attempted to use decision trees and random forests for its investigation as a part of machine intelligence tools. The models are compared based on performance metrics such as mean squared error, R-squared, and precision-recall curves. Results: The results indicate that machine learning models, particularly the random forest algorithm, can accurately forecast mobile phone sales with a good accuracy rate. Conclusion: This research work contributes to the existing literature on machine learning in general and sales forecasting in particular. It is providing insights and recommendations for future research exploring the other techniques under machine intelligence for the predictive modelling. The findings have practical implications for mobile phone companies seeking to optimize their production, marketing, and pricing strategies based on accurate sales predictions.