Abstract
This paper aims to explore approaches to machine learning for predictive analysis of sales. The ensemble learning technique known as stacking is considered to enhance the performance of the sales forecasting predictive model. A stacking methodology was studied to build a single model regression ensemble. The findings indicate that we can improve the performance of predictive sales forecasting models using stacking techniques. The concept is that it is useful to merge all these findings into one with various predictive models with different sets of features.
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Acknowledgements
We are grateful to the Department of Computer Science & Engineering (Software Engineering) at Delhi Technological University for presenting us with this research opportunity which was essential in enhancing learning and promote research culture among ourselves.
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Jindal, R., Jain, I., Saxena, I., Chaurasia, M.K. (2021). Prediction of Sales Using Stacking Classifier. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_5
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DOI: https://doi.org/10.1007/978-981-15-7106-0_5
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