Sales Prediction Using Linear and KNN Regression

Abstract

An important part of present-day business intelligence is sales prediction. Sales prediction can be termed a complex problem, and it gets harder in the case of lack of data or missing data values, and the presence of outliers. Sales prediction is more of a regression problem than time series. Using machine learning algorithms, we can find complicated patterns in the sales dynamics including various risk variables as well, using supervised machine learning methods. Sales forecasting plays a huge role in a company’s success. An accurate sales prediction model can help businesses find potential risks and make better knowledgeable decisions. This paper aims to analyze the Rossmann sales data using predictive models such as linear regression and KNN regression. An accurate sales prediction can benefit a business by helping save money on excess inventory, planning properly for the future, and increasing the profit earned. Thus, it is also important to evaluate the model using statistical methods like RMSE and MAPE. The results are used in understanding which is a more suitable classifier for sales prediction.