International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Sales Forecasting using Machine Learning - Linear Regression & Random Forest Regressor |
|---|---|
| ABSTRACT | By projecting future sales using prior data and market patterns, sales forecasting is necessary to corporate planning. Accurate forecasting facilitates better decision-making, resource optimisation, and inventory management for businesses. Large datasets and intricate sales patterns are often challenging for conventional forecasting methods to manage, which results in fewer accurate forecasts. An approach based on machine learning sales forecasting system utilising Random Forest Regressor and Linear Regression methods is displayed in this study. While Random Forest Regressor enhances prediction accuracy by managing non-linear connections and minimising overfitting, Linear Regression is used to examine the connection between the input variables and sales numbers. The system includes feature selection, data collecting, preprocessing, model training, and evaluation. Measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score are used to gauge performance. The system determines Which model is more effective for accurate sales prediction by comparing the two methods. The suggested method assists companies in enhancing marketing tactics, financial planning, and inventory control, ultimately boosting productivity and profitability. |
| AUTHOR | KAVYA G D, DR. PUJA SHASHI PG Student, Department of MCA, City Engineering College, Bengaluru, India Professor & HOD, Department of MCA, City Engineering College, Bengaluru, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405051 |
| pdf/51_Sales Forecasting using Machine Learning - Linear Regression & Random Forest Regressor.pdf | |
| KEYWORDS | |
| References | 1. Ahmed, M., "Predictive Analytics for Sales Forecasting in IT Enterprises Using Machine Learning Techniques," pp. 45–52, 2026. 2. "Machine Learning Applied to Sales Prediction Modelling: A Systematic Literature Review," Saptadi, R., Kumar, P., and Lee, J. Journal of Artificial Intelligence Research, vol. 18, no. 1, pp. 78–89, 2026. 3. "Integration of Time Series and Retail Machine Learning Models Sales Forecasting," Journal of Intelligent Systems International, pp. 112–120, 2026, Dar, S., Sharma, R., and Patel, K. 4. " Using Predictive Machine Learning Models Retail Sales," International Journal of Business Analytics, pp. 56–64, 2025, Mustapha, A., and Sithole, T. 5. "Sales Forecasting in Retail Using Advanced Machine Learning Algorithms," Malik, H., Verma, S., and Rao, P., Journal of Computer-Based Intelligence, pp. 34–41, 2024. 6. "Retail Sales Forecasting Using Deep Learning: Systematic Literature Review," Eglite, K., and Birzniece, I. Procedia Computer Science, vol. 200, pp. 150–158, 2022. |
