Product Sales Forecasting and Prediction Using Machine Learning Algorithm
Keywords:Machine Learning, forecasting, predictive model, K-NN, Regression model
Sales forecasting plays a crucial role for companies involved in various industries such as retailing, logistics, manufacturing, marketing, and wholesaling. The utilization of this approach enables companies to effectively allocate resources, accurately forecast sales revenue, and develop strategic plans that promote long-term organizational success. The conventional employment of statistical techniques for predicting sales at supermarkets has left numerous challenges unattended, leading to the development of models for prediction that exhibit suboptimal performance. The contemporary age of voluminous data, in conjunction with the availability of extensive computational resources, has rendered machine learning a preferred approach for predicting sales. This study demonstrates improved performance in predicting product sales from a specific store compared to existing machine learning algorithms. A K-Nearest Neighbor (K-NN) predictive model was constructed to anticipate sales for a business, specifically Big-Mart. The model's efficacy was then contrasted with that of Linear, Polynomial, and Ridge regression methodologies.
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