An Initial Survey on Sales prediction using Linear Regression

Sales prediction is a vital strategy for corporate decision-making, helping businesses optimize resource utilization, manage inventory, and enhance capital management. This study examines the application of linear regression as a predictive method in sales forecasting, utilizing historical data and various influencing factors. The research empirically evaluates the model's performance, emphasizing its strengths as well as its limitations in real-world business contexts. The study focuses on creating a robust sales prediction framework using linear regression, simplicity and interpretability. By identifying quantitative relationships between sales (the dependent variable) and key factors such as advertising spending, product pricing, seasonal fluctuations, and demographic characteristics, it provides strategic business planning.