ItemConference Paper

Leveraging ML Based Technique for Mobile Sales Forecasting

Title
Leveraging ML Based Technique for Mobile Sales Forecasting
Creator
Patel, Dharma; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Aruna, S.K.; Vinodha, D.
Description
The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi.
Source
Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Artificial Intelligence; Decision Tree; Forecasting; Machine Learning; Mobile
Coverage
Patel D., Soet, Christ University, Dept. of Cse, Bangalore, India; Sambandam R.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vetriveeran D., Soet, Christ University, Dept. of Cse, Bangalore, India; Jenefa J., Soet, Christ University, Dept. of Cse, Bangalore, India; Aruna S.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vinodha D., Soet, Christ University, Dept. of Cse, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-938054460-1;
Format
online
Language
English
Type
Conference paper

Citation

Patel, Dharma; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Aruna, S.K.; Vinodha, D., “Leveraging ML Based Technique for Mobile Sales Forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26237.

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