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- Vol. 7 Issue 2
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Sales Forecast Prediction Using Machine Learning
Authors
1 2 Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Patna Campus, Bihar, India.
Article Information
- Published In
- Volume 7, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 280-285
- Article Type
- Original Article
- Check for updates
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References
1. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD Conference.
3. Cover, T., & Hart, P. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
4. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley.
5. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
6. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly.
7. Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
8. Kaggle. (2018). Demand Forecasting Dataset. Retrieved from https://www.kaggle.com
9. Brownlee, J. (2017). Machine Learning Mastery with Python. Machine Learning Mastery.
10. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice.
11. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
12. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
13. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
14. Han, J., Kamber, M., & PeiMitchell, J. (2011). Data Mining: Concepts and Techniques. Elsevier.
15. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
16. Scikit-learn Documentation. (2023). https://scikit-learn.org
17. XGBoost Documentation. (2023). https://xgboost.readthedocs.io
2. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD Conference.
3. Cover, T., & Hart, P. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
4. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley.
5. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
6. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly.
7. Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
8. Kaggle. (2018). Demand Forecasting Dataset. Retrieved from https://www.kaggle.com
9. Brownlee, J. (2017). Machine Learning Mastery with Python. Machine Learning Mastery.
10. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice.
11. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
12. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
13. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
14. Han, J., Kamber, M., & PeiMitchell, J. (2011). Data Mining: Concepts and Techniques. Elsevier.
15. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
16. Scikit-learn Documentation. (2023). https://scikit-learn.org
17. XGBoost Documentation. (2023). https://xgboost.readthedocs.io
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