Exploring the role of feature engineering in enhancing machine learning models for sales prediction in the retail sector

SL Rajput; AM Tripathi; Manish Maurya

Exploring the role of feature engineering in enhancing machine learning models for sales prediction in the retail sector

Author(s)
SL Rajput, AM Tripathi and Manish Maurya

Abstract
In the realm of retail, the ability to predict sales accurately holds paramount importance for businesses striving to optimize inventory management, enhance customer experiences, and ultimately maximize profitability. This review paper delves into the pivotal role of feature engineering in refining machine learning models dedicated to sales prediction within the retail sector. Through a comprehensive examination of existing literature and empirical studies, we shed light on the transformative impact of feature engineering on model performance and predictive accuracy.

Feature engineering, as a critical phase in the machine learning pipeline, involves the strategic creation and manipulation of input variables to augment the model's ability to discern patterns and relationships within the data. In the context of retail sales prediction, the multitude of factors influencing consumer behavior necessitates a nuanced approach to feature engineering. This paper delineates various types of features, both traditional and domain-specific, that have demonstrated efficacy in capturing the intricate dynamics of the retail landscape.

The efficacy of machine learning models in sales prediction is contingent upon their capacity to assimilate and interpret diverse data sources. Feature engineering emerges as the linchpin in this process, facilitating the extraction of meaningful insights from raw data. We delve into the synergistic relationship between feature engineering and model architecture, elucidating how well-crafted features can mitigate issues such as overfitting and enhance the generalization capabilities of models.

Furthermore, the paper explores real-world applications and case studies where feature engineering has been instrumental in optimizing sales prediction models. From temporal features capturing seasonality effects to engineered variables encapsulating consumer sentiments, this review encapsulates a spectrum of feature engineering techniques that resonate with the unique challenges posed by the retail domain.

DOI:10.22271/allresearch.2018.v4.i8b.11447

Pages: 108-111 | 143 Views 62 Downloads

download (571KB)

IMPACT FACTOR (RJIF): 8.4

Vol. 4, Issue 8, Part B (2018)

Exploring the role of feature engineering in enhancing machine learning models for sales prediction in the retail sector

Related Journals

Related Journal Subscription

Important Publications Links