Machine learning models for forecasting and estimation of business operations

Machine Learning (ML) systems are built to shift through large amounts of data. Applying ML in production settings allows for the collection of additional data that can be used to guide future decisions about the system's design. Since the late 1970s, academics have taken an interest in the field of financial predictions. The real business environment has neglected statistical methods in forecasting, despite highly sophisticated models and rising competence in econometrics and economics studies. Current research centres on implementing various algorithms to identify the variation in performance for each product, and it compares the time series models to one another to identify the better model. A basic forecast model can make reliable, fact-based sales projections, as suggested by the books on forecasting. The worth of the forecast model lies in its ability to simplify the arduous tasks of budgeting and rolling forecasting by providing an unbiased forecast upon which a comprehensive financial strategy can be based. In this research, we first look for appropriate machine learning algorithms that can be used to predict sales of truck components, and then we run experiments with the selected algorithms to make predictions about sales and assess how well they work. Business forecasting allows for the estimation of a wide variety of activities, each of which can be tailored to the individual requirements of the company. Here are a few examples of frequently estimated kinds of operations. Although it is well-known that certain algorithms, such as Simple Linear Regression, Gradient Boosting Regression, Support Vector Regression, and Random Forest Regression, outperform others, it has been demonstrated that Random Forest Regression is the most suitable algorithm. Based on the results of the experiments and the analysis, the Ridge regression algorithm was selected as the best algorithm to conduct the sales forecasting of truck components for the selected data. © 2023 Elsevier Inc.