Smart Shopping Prediction on Smart Shopping With Linear Regression Method

IoT-based shopping needs can provide convenience in shopping. But this experience can be improved by the ability to predict the needs of goods. Until now the solution to this problem is not available. This is the reason we offer a Smart Shopping System. This system will classify food ingredients based on the amount of food stock in which the amount of stock is the result of the estimated time series data. The system will forecast each food supply by studying patterns of food use in the past using Linear Regression techniques. The system is implemented using Raspberry Pi, webcams, and barcode image processing for stock counting at home and a smartphone for the application dashboard. From a limited period of research, forecasting performance results that show that linear regression provides good accuracy in predictions.