Predictive Analytics for Business Growth and Decision Making: A Data-Driven Approach

The purpose of this study is to investigate the strategic use of predictive analytics by forecasting total sales from a supermarket sales dataset using ridge regression and numerical characteristics. Based on the idea that patterns in past data might influence present and future business choices, ridge regression was chosen because it effectively manages multi collinearity and improves prediction accuracy. After identifying the primary drivers of sales using feature correlation analysis, a ridge regression model was constructed as part of the technique. With a Mean Squared Error (MSE) of 0.00109 and an R-squared value of 0.99, the model showed remarkable performance. The findings showed that quantity, gross income, and unit pricing had the most effect on overall sales. According to these results, predictive models may be extremely helpful in enhancing the accuracy of decisions, allocating resources as efficiently as possible, and fostering strategic corporate expansion. Although the model performs well, its drawbacks include the dataset's limited breadth and the omission of categorical variables. This study offers novel insights by showing how predictive modeling may directly improve business strategy, expanding beyond operational forecasting.