Exploring Future Sales Prediction using Classification and Regression

Future forecast The greatest way to accomplish the targeted marketing objectives is through sales analysis. It would be better to advance your career once you have the capacity to make strategic decisions in sales forecasting. When predicting sales numbers, it is important to remember that future product prices will have an impact on the volume of sales in addition to past sales data. Multivariate time series first appear to be the best model for this problem. Since there is only ever one price for a product at any given time in sales history, unlike in real life where history is not always repeating. It makes creating a multivariate time series more challenging. However, the price is more dependent on the expiration date for some seasonal or perishable products. This additional data can aid in the creation of a causal time series model that is more precise. The proposed remedy makes use of a univariate time series model, but includes the product's price as a factor that systematically affects the prediction. Based on previous sales data, the price influence is calculated using data correlation analysis and customizable price ranges to find products with comparable histories. This unique strategy is simple to compute compared to other methods and enables the pricing parameter for simulations and forecasts to be chosen in advance